EFITA 2019
12th EFITA-HAICTA-WCCA CONGRESS
Rhodes, Greece, 27-29 June, 2019

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Sensors

Novel Proximal and Remote Sensing Approaches for Deriving Vegeration Indices: A Case Study Comparing PLANT-O-METER and SENTINEL-2 Data
Miloš Pandžić, Aristotelis C. Tagarakis, Vasa Radonić, Oskar Marko, Goran Kitić, Marko Panić and Nataša Ljubičić
Pages: 12-17

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ABSTRACT: With an increasing interest of the agricultural community in precision agriculture, this paper aims to compare two novel sensing approaches for crop monitoring. The recently developed multispectral proximal sensor named Plant-O-Meter and Sentinel-2 satellite, which carries a multispectral optical instrument,are two sensors suitable for agricultural applications. Each of them has pros and cons regarding spatial, spectral and temporal resolutions and their complementary use will surely bring added value compared to information retrieved by a single sensor. In order to correctly address the problem of data fusion, compatibility studies between the two sensors are necessary. In this study, a maize field was sensed on several dates in 2018growingseason using both sensors. Numerous vegetation indices based on different spectral channel combinations were calculated and the results were compared using linear regression analysis. First results showed good positive correlations between the indices obtained by the two sensors.

References

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  • Hatfield, J.L., Gitelson, A.A, Schepers, J.S. and Walthall C.L. (2008) ‘Application of Spectral Remote Sensing for Agronomic Decisions’, Agronomy Journal, 100, pp. 117–131. doi: 10.2134/agronj2006.0370c.
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  • Mulla, D. J. (2013) ‘Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps’, Biosystems Engineering, Special Issue: Sensing in Agriculture, pp. 358–371. doi: 10.1016/j.biosystemseng.2012.08.009.
  • Shanahan, J.F., Kitchen, N.R., Raun, W.R. and Schepers, J.S. (2008) ‘Responsive in-season nitrogen management for cereals’, Computers and Electronics in Agriculture, 61, pp. 51-62. doi: 10.1016/j.compag.2007.06.006.
  • Tagarakis, A. C. and Ketterings, Q.M. (2017) ‘In-Season Estimation of Corn Yield Potential Using Proximal Sensing’, Agronomy Journal, 109, pp. 1323-1330. doi: 10.2134/agronj2016.12.0732.
  • Wagner, P. and Hank, K. (2013) ‘Suitability of aerial and satellite data for calculation of site-specific nitrogen fertilisation compared to ground based sensor data’, Precision Agriculture, 14, pp. 135–150. doi: 10.1007/s11119-012-9278-1.
  • Woodcock, C. E., Allen, R., Anderson, M., Belward, A., Bindschadler, R., Cohen, W., Gao, F., Goward, S. N., Helder, D., Helmer, E., Nemani, R., Oreopoulos, L., Schott, J., Thenkabail, P. S., Vermote, E. F., Vogelmann, J., Wulder, M. A. and Wynne, R. (2008) ‘Free access to Landsat imagery’, Science, 320, pp. 1011. doi: 10.1126/science.320.5879.1011a.
  • Yang, C.-M., Liu, C.-C. and Wang, Y.-W. (2008) ‘Using Formosat-2 Satellite Data to Estimate Leaf Area Index of Rice Crop’, Journal of Photogrammetry and Remote Sensing, 13, pp. 253-260.

Use of a 3D Ιmaging Device to Model the Complete Shape of Dairy Cattle and Measure New Morphological Phenotypes
Clément Allain, Anaïs Caillot, Laurence Depuille, Philippe Faverdin, Jean-Michel Delouard, Laurent Delattre, Thibault Luginbuhl, Jacques Lassalas and Yannick Le Cozler
ages: 18-23

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ABSTRACT: Monitoring of body weight variation, body condition and/or morphological changes allows optimal management of animal health, production and reproduction performance. However, due to implementation difficulties (handling, time consumption, investments), this type of monitoring is not very common within commercial farms. The development of three-dimensional imaging technologies is an interesting solution to meet these needs. The purpose of this study was to develop, test and validate a device (Morpho3D) offering the possibility of recording and analysing complete 3D forms of dairy cattle. To evaluate the performance of this tool, manual measurements were performed on 30 Holstein dairy cows: height at withers (HG), chest circumference (TP), chest depth (PP), hip width (LH), buttock width (LF) and ischium width (LI). They were compared to those estimated by the Morpho3D device. Correlations coefficients between Morpho3D measurements and manual measurements were 0.89 for PP, 0.80 for LH, 0.78 for TP, 0.76 for LF, 0.63 for LI and 0.62 for HG. For the Morpho3D system, the repeatability standard deviation ranged from 0.34 to 1.89 (coefficient of variation (CV) from 0.26 to 9.81) and the reproducibility standard deviation ranged from 0.55 to 5.87 (CV from 0.94 to 7.34). These values are close to those obtained with manual measurements. This new device offers the possibility of measuring new phenotypes such as the total volume of the animal or the body surface and thus offers new opportunities for new researches and studies.

References

  • Buranakarl, C., Indramangala, J., Koobkaew, K., Sanghuayphrai, N., Sanpote, J., Tanprasert, C., Phatrapornnant, T., Sukhumavasi, W., Nampimoon, P., 2012. Estimation of body weight and body surface area in swamp buffaloes using visual image analysis. Journal of Buffalo Science. 1, 13–20.
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  • Fischer, A., Luginbuhl, T., Delattre, L., Delouard, J.M., Faverdin, P., 2015. Rear shape in 3 dimensions summarized by principal component analysis is a good predictor of body condition score in Holstein dairy cows. Journal of Dairy Science. 98, 4465–4476.
  • Guo, H., Ma, X., Ma, Q., Wang, K., Su, W., Zhu, D., 2017. LSSA_CAU: an interactive 3d point clouds analysis software for body measurement of livestock with similar forms of cows and pigs. Computers and Electronics in Agriculture. 138, 60–68.
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  • Kuzuhara, Y., Kawamura, K., Yoshitoshi, R., Tamaki, T., Sugai, S., Ikegami, M., Kurokawa, Y., Obitsu, T., Okita, M., Sugino, T., Yasuda, T., 2015. A preliminarily study for predicting body weight and milk properties in lactating Holstein cows using a three dimensional camera system. Computers and Electronics in Agriculture. 111, 186–193.
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Towards 5G Requirements: Performance Evaluation of a Simulated WSN Using SDN Technology
José Olimpio R. Batista Jr., Gustavo M. Mostaço, Roberto F. Silva, Graça Bressan, Carlos E. Cugnasca and Moacyr Martucci Jr.
Pages: 24-29

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ABSTRACT: The 5G, Fifth Generation of Mobile Networks, currently in its final development stage, promises to innovate the Internet of Things (IoT) ecosystem. It has the potential to aid in problem solving and improve the quality of existing and future services and applications. Some of the main applications include Wireless Sensor Networks (WSN), which may benefit from its very high speed and low latency in communications. Many services and applications related to WSNs are limited due to low speed and high latency connections. Some of its uses in agriculture range from fixed sensors networks forgathering weather data for irrigation control, to mobile WSNs with nodes attached to animals in the field, collecting health and productivity data, among many others. In this paper, we simulated an ad-hoc network with and without Software-Defined Networking (SDN) technology, to verify the average latency and packet delivery rate in conditions to support 5G requirements. To do so, COOJA and it-SDN were used as WSN simulators. It was observed that the use of SDN resulted in similar packet loss rate (1%) and in a considerably lower latency (at least 47%) compared to the other protocols evaluated.

References

  • Alves, R.C., Oliveira, D.A., Segura, G.N., Margi, C.B. (2017) ‘IT-SDN: Installation Guide (for Linux 64 bits)’ -March.
  • Condotta, I.C.F.S. et al. (2018) ‘Using an artificial neural network to predict pig mass from depth images’. In: 10th International Livestock Environment Symposium (ILES X). American Society of Agricultural and Biological Engineers, pp. 1.
  • ITU-T (2012) ‘Y.2060 -Overview of the Internet of Things. Recommendation’, ITU-T. https://doi.org/10.1021/ic00245a007.
  • Kaur, K. et al. (2018) ‘Edge Computing in the Industrial Internet of Things Environment: Software-Defined-Networks-Based Edge-Cloud Interplay’, Communications Magazine, 56(2), pp. 44-51. ISSN 1558-1896.
  • Mehmood, T. (2017) ‘COOJA Network Simulator: Exploring the InfinitePossible Ways to Compute the Performance Metrics of IOT Based Smart Devices to Understand the Working of IOT Based Compression and Routing Protocols’, Dept. of Electrical Engineering, SEECS, NUST Islamabad.
  • Norton, T., Berckmans, D. (2018) ‘Precision Livestock Farming: the Future of Livestock Welfare Monitoring and Management?’. Animal Welfare in a Changing World, pp. 130
  • Parvez, I. et al. (2018) ‘A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions’, IEEE Communications Surveys & Tutorials, 20(4), pp. 3098-3130. doi: 10.1109/COMST.2018.2841349.
  • Sandano, I. (2018) ‘The Self-Driven Network, A trajetória rumo à automação e seus estágios ́, São Paulo, Brasil.
  • Yousaf, F. Z. et al. (2017) ‘NFV and SDN-Key technology enablers for 5G networks’, IEEE Journal on Selected Areas in Communications, 35(11), pp. 2468–2478. doi: 10.1109/JSAC.2017.2760418.
  • Zanella et al. (2014) ‘Internet of Things for Smart Cities ́, IEEE Internet of Things,’ 1(1), pp. 22-32. ISSN: 2327-4662

Preliminary Results of Multispectral Camera Mounted on Unmanned Aerial Vehicle for Soil Properties Estimation and Mapping
Theodora Angelopoulou, Nikolaos Tziolas, Athanasios Balafoutis, Georgios Zalidis and Dionysis Bochtis
Pages: 30-35

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ABSTRACT: Soil quality continuously deteriorates due to extensive agricultural practices, risking food security. Thus, soil quality sustainability is vital to extend agricultural land productivity potential. However, soil properties estimation entails time consuming, laborious and expensive procedures. Smart agriculture schemes include novel and potentially low cost in situ observations such as unmanned aerial vehicles(UAVs)that are rapidly maturing and becoming viable alternatives to costlier traditional solutions for digital soil mapping. The objective of this research was to evaluate the capabilities of multispectral imagery (400-810 nm) predictive ability for soil properties estimation acquired in bare soil conditions in a 6-haexperimental field in Rizomilos, Thessaly, Greece. A comparative analysis was performed with laboratory spectral measurements of 18 soil samples (0–30 cm) collected from the same field covering the complete VNIR-SWIR range (400-2500 nm). The soil samples were also determined by wet chemistry methods to calibrate the developed prediction models. Considering the imagery data values, the laboratory spectral signatures and the produced spectral indices as input features, a support vector machine for regression algorithm(SVR) was used for model calibration.Laboratory soil spectroscopy resulted in R2= 0.58while UAV application R2= 0.48.

References

  • Aldana-Jague, E.,Heckrath, G.,Macdonald, A.,van Wesemael, B.,Van Oost, K. (2016) ‘UAS-based soil carbon mapping using VIS-NIR (480-1000 nm) multi-spectral imaging: Potential and limitations’, Geoderma, 275, pp. 55–66. doi: 10.1016/j.geoderma.2016.04.012
  • Angelopoulou, T.,Tziolas, N.,Balafoutis, A.,Zalidis, G.,Bochtis, D.(2019) ‘Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review’, Remote Sensing, 11(6), p. 676. doi: 10.3390/rs11060676.
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  • Ben Dor, E., Ong, C. and Lau, I. C. (2015) ‘Reflectance measurements of soils in the laboratory: Standards and protocols’, Geoderma, 245–246. doi: 10.1016/j.geoderma.2015.01.002.
  • Gholizadeh, A.,Žižala, D.,Saberioon, M.,Borůvka, L.(2018) ‘Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging’, Remote Sensing of Environment, 218(September), pp. 89–103. doi: 10.1016/j.rse.2018.09.015.
  • Levin, N., Kidron, G. J. and Ben-Dor, E. (2007) ‘Surface properties of stabilizing coastal dunes: combining spectral and field analyses’, Sedimentology. John Wiley & Sons, Ltd (10.1111), 54(4), pp. 771–788. doi: 10.1111/j.1365-3091.2007.00859.x.
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  • Soriano-Disla, J.M., Janik, L.J., Viscarra Rossel, R.A., Macdonald, L.M.,McLaughlin, M.J.(2014) ‘The Performance of Visible, Near-, and Mid-Infrared Reflectance Spectroscopy for Prediction of Soil Physical, Chemical, and Biological Properties’, Applied Spectroscopy Reviews. Taylor & Francis, 49(2), pp. 139–186. doi: 10.1080/05704928.2013.811081.
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In-Field Testing of New Low-Cost Multispectral sensor for assessing maize yield potential
Aristotelis C. Tagarakis, Marko Kostić, Natasa Ljubičić, Bojana Ivošević, Goran Kitić and Miloš Pandžić
Pages: 36-41

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ABSTRACT: Active proximal sensing has been increasingly used to provide information about canopy properties in a large range of crops. In this study a low cost, active multispectral optical device named Plant-O-Meter (POM) was tested in real conditions at two experimental fields comparing it with the Green Seeker handheld device. Treatments included five nitrogen (N) fertilisation rates applied during sowing. Maize was scanned between V5 to V8 growth stages. The results showed that measuring with the POM sensor within this growth stage window can provide good estimation of end-of-season yield, comparable to the Green Seeker. This indicates that Plant-O-Meter exhibits strong potential for accurate plant canopy measurements and for real time variable rate fertilisation applications in maize.

References

  • Bean, G. M., Kitchen, N. R., Camberato, J. J., Ferguson, R. B., Fernandez, F. G., Franzen, D. W., Laboski, C. A. M., Nafziger, E. D., Sawyer, J. E., Scharf, P. C., Schepers, J. and Shanahan, J. S. (2018)‘Active-optical reflectance sensing corn algorithms evaluated over the United States Midwest corn belt’,Agronomy Journal,110, pp. 2552–2565.
  • Hatfield, J.L., Gitelson,A.A., Schepers, J.S. and Walthall, C.L.(2008)‘Application of spectral remote sensing for agronomic decisions’, Agronomy Journal, 100, pp. 117–131. doi:10.2134/agronj2006.0370c.
  • Kim, Y., Huete, A., Miura, T. andJiang, Z. (2010)‘Spectral compatibility of vegetation indices across sensors: band decomposition analysis with Hyperion data’,Journal of Applied Remote Sensing 4(1) 043520.doi: 10.1117/1.3400635.
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  • Lukina, E. V., Freeman, K. W., Wynn, K. J., Thomason, W. E., Mullen, R. W., Stone, M. L., Solie, J. B., Klatt, A. R., Johnson, G. V., Elliott, R. L. and Raun, W. R. (2001)‘Nitrogen fertiliyation optimization algotithmbased on in-season estimates of yield and plant nitrogen uptake’, Journal of Plant Nutrition,24(6), pp.885-898.doi: 10.1081/PLN-100103780
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  • Moges, S. M., Girma, K., Teal, R. K., Freeman, K. W., Zhang, H. andArnall, D. B. (2007)‘In-season estimation ofgrain sorghum yield potential using a hand-held optical sensor’,Arch. of Agron. and Soil Sci.,53(6),pp. 617–628. doi:10.1080/03650340701597251.
  • Raun, W.R., Solie, J.B., Johnson, G.V., Stone, M.L., Mullen, R.W. and Freeman, K.W. (2002)‘Improving nitrogen use efficiency in cereal grain production with optical sensing andvariable rate application’, Agron. J.,94, pp.815–820. doi: 10.2134/agronj2002.8150.
  • Raun, W. R., Solie, J.B., Martin, K.L., Freeman, K.W., Stone, M. L., Johnson, G.V. and Mullen, R.W.(2005)‘Growth stage, development, and spatial variability in corn evaluated using optical sensor readings’,J. Plant Nutr.,28, pp.173-182.doi: 10.1081/PLN-200042277.
  • Rogers, N. G. (2016)‘Sensor Based Nitrogen Management for Corn Production in CoastalPlain Soils’,All Theses. 2579.
  • Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D.W. (1973)‘Monitoring vegetation systems in the Great Plains with ERTS’,NASA. Goddard Space Flight Center 3d ERTS-1 Symp., 1,pp. 309–317.
  • Solari, F., Shanahan, J., Ferguson, R. B., Schepers, J. S. andGitelson, A. A. (2008) ‘Active sensor reflectance measurements to corn nitrogen status and yield potential’,Agronomy Journal,100, pp.571–579.doi: 10.2134/agronj2007.0244.
  • Tagarakis A. C. and Ketterings Q. M. (2017) ‘In-season estimation of corn yield potential using proximal sensing’,Agronomy Journal,109(4), pp.1323-1330. doi: 10.2134/agronj2016.12.0732.
  • Teal, R. K., Tubana, B., Girma, K., Freeman, K. W., Arnall, D. B., Walsh, O. and Raun, W. R. (2006)In-season prediction of corn grain yield potential using normalized difference vegetation index’, Agron. J.,98,pp. 1488–1494. doi:10.2134/agronj2006.0103
  • Tremblay N., Wang Z., Ma, B. L., Belec, C. andVigneault, P. (2009)‘A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application’,Precision Agriculture,10, pp.145-161. doi: 10.1007/s11119-008-9080-2
  • Wang, R., Cherkauer, K. A. and Bowling, L. C. (2016) ‘Corn Response to Climate Stress Detected with Satellite-Based NDVI Time Series’,Remote Sensing,8(4),pp. 269.doi: 10.3390/rs8040269.
  • Yao, X., Yao, X., Jia, W., Tian, Y., Ni, J., Cao, W.andZhu, Y. (2013) ‘Comparison and intercalibration of vegetation indices from different sensors for monitoring above-ground plant nitrogen uptake in winter wheat’, Sensors, 13(3), pp. 3109-3130.doi: 10.3390/s130303109.
  • Zecha, C. W., Peteinatos, G. G., Link, J.and Claupein, W. (2018) ‘Utilisationof ground and airborne optical sensors for nitrogen level identification and yield prediction in wheat’, Agriculture,8(6) pp. 79.doi: 0.3390/agriculture8060079

OPTIMA - OPTimised Integrated Pest MAnagement for Precise Detection and Control of Plant Diseases in Perennial Crops and Open-Field Vegetables
Athanasios Balafoutis, Nikos Mylonas, Spyros Fountas, Dimitris Tsitsigiannis, Paolo Balsari, Massimo Pugliese, Emilio Gil, David Nuyttens, Gerrit Polder, Fausto Freire,Jose Paulo Sousa,. Mathilde Briande, Valerie Le Clerc, Jean-Paul Douzals, Amedeo Caffini, Lars Berger, Zisis Tsiropoulos, Daniele Eberle, Sarah Bellalou and Andreas Thierfelder
Pages: 42-47

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ABSTRACT: OPTIMA is an H2020 research project that will develop an environmentally friendly IPM framework for vineyards, apple orchards and carrots by providing a holistic integrated approach which includes all critical aspects related to integrated disease management, such as i) use of novel biological Plant Protection Products, ii) disease prediction models, iii) spectral early disease detection systems and iv) precision spraying techniques. It will contribute significantly to the reduction of the European agriculture reliance on chemical Plant Protection Products resulting in reduced use of agrochemicals, lower residues and reduced impacts on human health.

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Data

Deep Learning Based Plant Part Detection in Greenhouse Settings
Manya Afonso, Ruud Barth and Aneesh Chauhan
Pages: 48-53

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ABSTRACT: Precision agriculture challenges such as automatic harvesting, phenotyping, and yield prediction require precise detection of plant parts such as the fruits, leaves or stems. Deep learning has emerged as the state-of-the-art technology for image segmentation and object detection in several domains, notably in self-driving vehicles and medical imaging. In recent years, deep learning methods are being increasingly adopted in vision-based applications for precision agriculture. In previous work, methods were investigated to segment the image for plant parts. However, such an approach did not yield object instances. In this work, we applied the state-of-the-art deep learning object detector, MaskRCNN, to the problem of detecting fruit and other plant parts, in the sweet pepper (capsicum annuum) plant. An extensive study was carried out where we investigated different transfer learning schemes, different convolutional neural network architectures, and varying numbers of training images. Experimentally, we found that MaskRCNN trained with the synthetic data and fine-tuned with very few empirical images is able to detect more than 95% of the sweet pepper fruit. It was also found that training on the synthetic data and then fine-tuning over a few empirical images led to a better performance in the detection of fruit, over training only on the limited set of empirical images. Furthermore, results show that the best model could successfully generalize to different imaging conditions. This work is a necessary step for applying deep learning in high-throughput robotics and phenotyping approaches and will open up many opportunities for smart farming and more efficient use of resources. Currently, training deep learning models is dependent on the knowledge and expertise of the scientists involved. The insights gained from this work should lead to more automatic training protocols, allowing widespread use in very different applications.

References

  • Bac, C. W. (2015). Improving obstacle awareness for robotic harvesting of sweet-pepper. PhD thesis, Wageningen University and Research.
  • Bac, C. W., van Henten, E. J., Hemming, J., and Edan, Y. (2014). Harvesting robots for high-value crops: State-of-the-art review and challenges ahead. Journal of Field Robotics, 31(6):888–911.
  • Barth, R. (2018). Vision principles for harvest robotics : sowing artificial intelligence in agriculture. PhD thesis, WageningenUniversity and Research Barth, R., IJsselmuiden, J., Hemming, J., and E.J. Van Henten (2017). Synthetic bootstrapping of convolutional neural networks for semantic plant part segmentation. Computers and Electronics in Agriculture.
  • Barth, R., IJsselmuiden, J., Hemming, J., and E.J. Van Henten (2018). Data synthesis methods for semantic segmentation in agriculture: A capsicum annuum dataset. Computers and Electronics in Agriculture, 144:284 –296.
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  • Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K. (2017). Aggregated residual transformations for deep neural networks. In Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on, pages 5987–5995. IEEE.

Exploring OWA Operators for Aggregating Fuzzy Cognitive Maps Constructed By Experts/Stakeholders in Agriculture
Konstantinos Papageorgiou, Elpiniki Papageorgiou, Asmaa Mourhir and George Stamoulis
Pages: 54-59

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ABSTRACT: Fuzzy cognitive maps are graph-based models mostly constructed in a participatory setting either by experts to given domains or by a large number of stakeholders in various scientific areas of interest. Usually each expert or stakeholder designs individually a FCM based on his/her knowledge or opinion for the specific application domain. For scenario analysis and decision-making purposes, an overall FCM for the specific problem needs to be constructed, aggregating all individual FCMs designed by the experts and/or stakeholders. The average aggregation method for weighted interconnections among concepts is the most common method in FCM modeling, which is quite simple,regardless the inherent uncertainty induced by the different experts’ or stakeholders’ opinions. The aim of this research work is two-fold: (i) to propose an alternative aggregation method based on learning Ordered Weighted Average(OWA)operators in aggregating FCM weights,assigned by many experts and/or stakeholders,and (ii) to present a new software tool for FCM aggregation, called FCM-OWA, leveraging the different FCM aggregation methods. A precision farming problem,considering apple yield prediction,is used to show the applicability and usefulness of the proposed methodology in modeling.The results after comparing OWA operators with the traditional average aggregation method imply that the proposed approach is really challenging on modeling experts’ knowledge in agricultural domain.

References

  • Axelrod, R. (1976). Structure of Decision. Princeton, USA: Princeton University Press.
  • Filev, D. and Yager, R.R. (1998). On the issue of obtaining OWA operator weights. Fuzzy Sets and Systems. 94, 157-169.
  • Gray, S.A., Zanre, E.andGray, S.R.J. (2014). Fuzzy Cognitive Maps as Representations of Mental Models and Group Beliefs, In: Papageorgiou, E.I. (Ed.), Fuzzy Cognitive Maps for Applied Sciences and Engineering. Springer Berlin Heidelberg, pp. 29-48.
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  • Leyva-Vαzquez,M.,Pιrez-Teruel,K. and John,R. (2014).A Model for Enterprise Architecture Scenario Analysis Based on Fuzzy Cognitive Maps and OWA Operators, IEEE conference.
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  • Papageorgiou,E. andStylios,C. (2008). Fuzzy cognitivemaps. Handbook of granular computing. pp. 755-774. Chichester, England: John Wiley and Son Ltd, Publication Atrium.
  • Papageorgiou, E.I., Aggelopoulou, K.D., Gemtos, T.A. and Nanos,G.D. (2013).Yield prediction in apples using Fuzzy Cognitive Map learning approach.Computers &Electronics in Agriculture.91,19-29.
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Grapevine Yield Prediction Using Image Analysis - Improving the Estimation of Non-Visible Bunches
Gonçalo Victorino, Guilherme Maia, José Queiroz, Ricardo Braga, Jorge Marques, José Santos-Victor and Carlos Lopes
Pages: 60-65

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ABSTRACT: Yield forecast is an issue of utmost importance for the entire grape and wine sectors.There are several methods for vineyard yield estimation. The ones based on estimating yield components are the most commonly used in commercial vineyards.Those methods are generally destructive and very labor intensive and can provide inaccurate results as they are based on the assessment of a small sample of bunches. Recently, several attempts have been made to apply image analysis technologies for bunch and/or berries recognition in digital images. Nonetheless, the effectiveness of image analysis in predicting yield is strongly dependent of grape bunch visibility, which is dependent on canopy density at fruiting zone and on bunch number, density and dimensions. In this work data on bunch occlusion obtained in a field experiment is presented. This work is set-up in the frame of a research project aimed at the development of an unmanned ground vehicle to scout vineyards for non-intrusive estimation of canopy features and grape yield. The objective is to evaluate the use of explanatory variables to estimate the fraction of non-visible bunches (bunches occluded by leaves). In the future, this estimation can potentially improve the accuracy of a computer vision algorithm used by the robot to estimate total yield. In two vineyard plots with Encruzado (white) and Syrah (red) varieties,several canopy segments of 1 meter length were photographed with a RGB camera and a blue background, close to harvest date. Out of these images, canopy gaps (porosity) and bunches’ region of interest (ROI) files were computed in order to estimate the corresponding projected area. Vines were then defoliated at fruiting zone, in two steps and new images were obtained before each step. Overall the area of bunches occluded by leaves achieved mean values between 67% and 73%, with Syrah presenting the larger variation. A polynomial regression was fitted between canopy porosity (independent variable) and percentage of bunches notoccluded by leaves which showed significant R2values of 0.83 and 0.82for the Encruzado and Syrah varieties, respectively.Our results show that the fraction of non-visible bunches can be estimated indirectly using canopy porosity as explanatory variable, a trait that can be automatically obtained in the future using a laser range finder deployed on the mobile platform.

References

  • Aquino, A. et al.(2018) ‘Automated early yield prediction in vineyards from on-the-go image acquisition’, Computers and Electronics in Agriculture. doi: 10.1016/j.compag.2017.11.026.
  • De Bei, R. et al.(2016) ‘Viticanopy: A free computer app to estimate canopy vigor and porosity for grapevine’, Sensors (Switzerland). doi: 10.3390/s16040585.
  • Besselat, B. (1987) ‘Les prévisions de récolte en viticulture’, 1985(Tableau 1), pp. 1–12
  • Bramley, R. G. V and Hamilton, R. P. (2004) ‘Understanding variability in winegrape production systems 2. Within vineyard variation in quality over several vintages’, Australian Journal Of Grape And Wine Research, 10(1), pp. 32–45. doi: 10.1111/j.1755-0238.2004.tb00006.x.
  • Cunha, M., Ribeiro, H. and Abreu, I. (2016) ‘Pollen-based predictive modelling of wine production: Application to an arid region’, European Journal of Agronomy. Elsevier B.V., 73, pp. 42–54. doi: 10.1016/j.eja.2015.10.008.
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  • Fraga, H. and Santos, J. A. (2017) ‘Daily prediction of seasonal grapevine production in the Douro wine region based on favourable meteorological conditions’, Australian Journal of Grape and Wine Research. doi: 10.1111/ajgw.12278.
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  • Milella, A. et al.(2019) ‘In-field high throughput grapevine phenotyping with a consumer-grade depth camera’, Computers and Electronics in Agriculture. Elsevier, 156(November 2018), pp. 293–306. doi: 10.1016/j.compag.2018.11.026.
  • Millan, B. et al.(2018) ‘On-the-Go Grapevine Yield Estimation Using Image Analysis and Boolean Model’. doi: 10.1155/2018/9634752.
  • Nuske, S. et al.(2014) ‘Automated visual yield estimation in vineyards’, in Journal of Field Robotics. doi: 10.1002/rob.21541.
  • Pérez-Zavala, R. et al.(2018) ‘A pattern recognition strategy for visual grape bunch detection in vineyards’, Computers and Electronics in Agriculture. Elsevier, 151(September 2017), pp. 136–149. doi: 10.1016/j.compag.2018.05.019.
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A Datahub for Semantic Interoperability in Data-driven Integrated Greenhouse Systems
Jack Verhoosel, Barry Nouwt, Roos Bakker, Athanasios Sapounas and Bart Slager
Pages: 66-71

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ABSTRACT: This paper deals with the challenge of semantic alignment of different data sources in the horticultural sector. In this sector,greenhouses are used to grow vegetables and plants and the main goal for a greenhouse grower is to control the climate such that crop is optimally cultivated against the lowest cost.Combining available data sources to extract trends and patterns via data analysis, it is important to better support growing decisions.A Common Greenhouse Ontology (CGO) has been developed and used in a Data hub to make data sources accessible via Resource Description Framework (RDF)and a SPARQL interface on top of an Apache Jena Fuseki triplestore. The Data hub was applied in a trial use case in which three data sources where made accessible for a linear regression component that derived patterns between nutrients used and crop growth. One of the lessons learned is that the use of a common ontology very well supports the aligned use of data in analysis and thus better supports decision making.

References

  • Rehman A.U. (2015)‘Smart Agriculture: An Approach Towards Better Agriculture Management’, OMICS group eBooks, https://www.esciencecentral.org/ebooks/smart-agriculture-an-approach-towards-better-agriculture-management/pdf/agricultural-ontologies.pdf, Foster City, USA.
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Earth Observation Data and Spatial Data Sets Analysis
Pavel Simek, Jan Jarolimek, Eva Kanska, Michal Stoces, Jiri Vanek, Jan Pavlik and Alexandr Vasilenko
Pages: 72-77

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ABSTRACT: Aerial or satellite imagery allows for non-destructive remote sensing and monitoring in agriculture and related fields. The advantage of data from Sentinel missions is their availability and regular acquisition (approximately every 3 days in the Czech Republic). For effective remote sensing, there are often some limitations resulting from the properties of the acquired data –large data transmission (especially in the case of mobile use), resolution, cloudiness, sunlight reflections, etc. The research was focused on data sets from SENTINEL-2 Level-2A and higher, which are already pre-processed, especially in cartographic projection (UTM / WGS1984), creating tiles 100 x 100 km with atmospheric corrections with subsequent possibility to calculate vegetation indices, especially NDVI, GNDVI, LAI, EVI,RENDVI and MSI. Due to the size of the data, the usable data sets (area of interest and without clouds) are divided into a 5x5 km tile network grid, calculated vegetation indices and saved in data storage.The main goal is to propose a solution for the calculation of indexes from smaller data sets, to design a prototype and to subsequently verify the solution on a pilot.

References

  • Arekhi, M.,Goksel, C.,Sanli, F. B. andSenel, G. (2019) ‘Comparative Evaluation of the Spectral and Spatial Consistency of SENTINEL-2and Landsat-8 OLI Data for Igneada Longos Forest’, ISPRS International Journal of Geo-information, vol. 8, issue 2, article no. 56. doi: 10.3390/ijgi8020056.
  • Baetens, L., Desjardins, C. and Hagolle, O. (2019) ‘Validation of Copernicus SENTINEL-2Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure’, Remote Sensing, vol. 11, issue 4, article no. 433, EISSN 2072-4292.doi: 10.3390/rs11040433.
  • Carrasco, L.,O'Neil, A. W.,Morton, R. D. andRowland, C. S. (2019) ‘Evaluating Combinations of Temporally Aggregated Sentinel-1, SENTINEL-2and Landsat 8 for Land Cover Mapping with Google Earth Engine’, Remote Sensing, vol. 11, issue 3, article no. 288. doi: 10.3390/rs11030288.
  • Defourny, P.,Bontemps, S.,Bellemans, N.,Cara, C.,Dedieu, G.,Guzzonato, E.,Hagolle, O.,Inglada, J.,Nicola, L,.Rabaute, T.,Savinaud, M.,Udroiu, C.,Valero, S.,Begue, A.,Dejoux, J. F.,El Harti, A.,Ezzahar, J.,Kussul, N.,Labbassi, K.,Lebourgeois, V.,Miao, Z.,Newby, T.,Nyamugama, A.,Salh, N.,Shelestov, A.,Simonneaux, V.,Traore, P. S.,Traore, S. S. andKoetz, B. (2019) ‘Near real-time agriculture monitoring atnational scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world’, Remote Sensing of Environment, vol. 221, pp. 551-568. doi: 10.1016/j.rse.2018.11.007
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  • Deng, L.,Mao,ZH.,Li, XJ.,Hu, ZW.,Duan, FZandYan, YN. (2018) ‘UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras’, ISPRS Journal of Photogrammetr and Remote Sensing, vol. 146, pp. 124-136. doi: 10.1016/j.isprsjprs.2018.09.008.
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  • Halabuk, A.,Mojses, M.,Halabuk, M and David,S. (2015) ‘Towards Detection of Cutting in Hay Meadows by Using of NDVI and EVI Time Series’, Remote Sensing, vol. 7, issue 5, pp. 6107-6132. doi: 10.3390/rs70506107.
  • Lillesand, T. M., Kiefer, R. W.andChipman, J. W.(2008)‘Remote sensing and image interpretation’,6thissue,Hoboken, NJ: John Wiley and Sons. ISBN-10: 0470052457, ISBN-13: 978-0470052457.
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Image Extraction Based on Depth Information for Calf Body Weight Estimation
Naoki Fukuda, Takenao Ohkawa, Chikara Ohta, Kenji Oyama, Yumi Takaki and Ryo Nishide
Pages: 78-83

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ABSTRACT: This paper aims to facilitate body weight estimation by using calf’s images taken in a loose barn. In our method, the procedures from image extraction of calves to the resultant body weight estimation are automated. The images with a single calf are used for body weight estimation. Most of the images are unusable, as several or none of the calves are included in the images, otherwise,the body parts are not properly extracted due to the calf’s posture. In this paper, we propose a method to select only the images appropriate for body weight estimation.First, the information such as the calf’s posture, body information and the angle of the calf to the camera are obtained. Then, this information is examined based on a certain threshold to extract only the appropriate images. Depth images are used because they are less affected by the surrounding environments and are considered useful for extracting calf area. The calf area is extracted by using background subtraction with a depth image. The images which meet all criteria are chosen as appropriate images for body weight estimation. Efficiency of the proposed automated method and manual work are compared by MAPE(Mean Absolute Percentage Error) of estimated calf weight. The MAPE by using manually-selected images was 10.34% and that by using proposed method was 13.94%, which yields the difference of 3.6%. From this result, we confirmed that the proposed method for automatically selecting appropriate images for body weight estimation can fairly perform as well as manual selection and can be effective to reduce human effort.

References

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Big Data Management Tools for Smart Farming Data
Michal Stočes, Pavel Šimek, Edita Šilerová, Jan Masner and Jan Jarolímek
Pages: 84-89

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ABSTRACT: This paper deals with the issue of role of middle ware in the process of data transformation, aggregation, storage and analysis. Data used in the concept of precision agriculture is very diverse. Not only in terms of sources but also formats. Farms use small sensor data as well as large files from satellite systems for various analyzes. Selected middle ware services will be evaluated using a multiple-criteria decision analysis.In agriculture, the collected data need to be continuously analyzed and worked with. Data processing procedures, especially with regard to Big Data, are not yet properly addressed in agriculture. As the volume of data collected grows, the demands for efficient storage grows as well. It is necessary to deal with this issue. The farmer's data sources can be divided between the data acquired by the farm from its own internal, private data source and data obtained externally. External data can be used from public open data databases or purchased.On the basis of a multiple-criteria decision analysis of the weighted sum method, key criteria, selected weights for the use of a small start-up company, Things Board was released as a compromise option with the highest value of 0.9. The second was Device Hive with 0.833 and Mainflux, WSo2 IoT and Thinger.io.

References

  • Atzori, L.,Iera, A.and Morabito, G. (2010). ‘The Internet of Things: A Survey‘. Computer networks, 54(15), pp. 2787-2805. doi: 10.1016/j.comnet.2010.05.010
  • Cimino, L.D.,de Resende, J.E.E.,Silva, L.H.M. et al. (2019) ‘A middleware solution for integrating and exploring IoT and HPC capabilities‘, Software: Practice and Experience, 49(4), pp. doi: 10.1002/spe.2630
  • Dastjerdi, A. V. and Buyya.,R. (2016). ‘Fog Computing: Helping the Internet of Things Realize Its Potential‘. Computer, 49(8), pp. 112-116. doi: 10.1109/MC.2016.245
  • da Cruz, M. A. A., Rodrigues, J. J. P. C., Al-Muhtadi,J., Korotaev,V. V. and de Albuquerque, V. H. C. (2018) ‘A Reference Model for Internet of Things Middleware‘ IEEEInternet of Things Journal 5(2), pp.871-883. doi:10.1109/JIOT.2018.2796561
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  • Jarolímek, J., Pavlík, J., Kholova, J. and Ronanki, S. (2019) ‘Data Pre-processing for Agricultural Simulations‘, AGRIS on-line Papers in Economics and Informatics, 11(1), pp. 49-53. doi: 10.7160/aol.2019.110105.
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  • Razzaque,M. A.,Milojevic-Jevric, M.,Palade, A.,and Clarke, S. (2016) ‘Middleware for Internet of Things: A Survey‘ IEEE Internet of Things Journal, 3(1), pp. 70-95. doi: 10.1109/JIOT.2015.2498900
  • Šimek P., Vaněk J., Stočes M., Jarolímek J.andPavlík J. (2017) ‘Mobile accessibility expense analysis of the agrarian WWW portal‘ Agricultural Economics, 63, pp. 197-203. doi: 10.17221/313/2015-AGRICECON
  • Somani, G., Zhao, X. Srirama, S. N.andBuyya, R. (2019) ‘Integration of Cloud, Internet of Things, and Big Data Analytics‘ Software: Practice and Experience, 49(4), pp 561-564. doi: 10.1002/spe.2664.
  • Stočes, M., Masner, J., Jarolímek, J., Šimek, P.(2018)‘Internet of Things and Big Data Processing in Agriculture.‘In AFITA/WCCA 2018 Proceedings -Research Frontiers in Precision Agriculture 24.10.2018, Powai, Mumbai, pp. 49-51.
  • Stočes, M., Šilerová, E., Vaněk, J., Jarolimek, J.andŠimek, P. (2018 ) ‘Possibilities of using open data in sugar & ugar beet sector‘ Listy cukrovarnické a řepařské, 134(3), pp. 117-121. ISSN: 1210-3306.
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Proposal of an Architecture for Data Integration at Agricultural Supply Chains, Considering the Implementation of IoT, NoSQL and Blockchain Technologies
Roberto F. Silva, Gustavo M. Mostaço, Fernando Xavier, Antonio Mauro Saraiva and Carlos E. Cugnasca
Pages: 90-95

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ABSTRACT: Agricultural supply chains produce a huge amount of data related to traceability, production processes, environmental monitoring, among others. These are very important for the decision-making processes of the different supply chain stakeholders. With the implementation of technologies related to the Internet of things (IoT), the quantity and variety of data generated will increase even further. In order to extract useful information in an efficient way, it will be important to consider many aspects related to data management, such as security, processing, storage, transfer, etc. In this paper, we gather the requirements to implement IoT on agricultural SCs and propose an architecture that uses local databases to store raw and confidential data, a No SQL database on the cloud to aggregate data that is important for decision-making by different stakeholders, and a blockchain that aggregates and safely stores the information for two main users. They are (i) consumers, considering aspects that guarantee product quality, together with what is relevant for them to choose between different products or brands; and (ii) government, which is related to data used for inspections, quality control, customs processes, and certifications. We conclude this paper by presenting at which level each of the functional, non-functional and domain-specific requirements will be fulfilled, and its main advantages in comparison with other architectures.

References

  • Atzori, L., Iera, A., Morabito, G. (2010) ‘The internet of things: A survey’, Computer Networks, 54(15), pp.2787-2805.
  • Carrez, F., Bauer, M., Boussard, M., Bui, N. (2013) ‘Final architectural reference model for the IoT v3. 0’, EC FP7 IoT-A Deliverable, 1.
  • Chopra, S., Meindl, P. (2013) ‘Supply chain management: Strategy, planning, and operation’, 5thed. New Jersey, USA: Pearson Education, 528pp.
  • Corella, V.P., Rosalen, R.C., Simarro, D.M. (2013) ‘SCIF-IRIS framework: a framework to facilitate interoperability in supply chains’, International Journal of Computer Integrated Manufacturing, 26(1-2), pp.67-86.
  • Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M. (2013) ‘Internet of Things (IoT): A vision, architectural elements, and future directions’, Future Generation Computer Systems, 29(7), pp.1645-1660.
  • Hackius, N., Petersen, M. (2017) ‘Blockchain in logistics and supply chain: trick or treat?’, In: Proceedings of the Hamburg International Conference of Logistics (HICL), pp. 3-18.
  • Hribernik, K.A., Warden, T., Thoben, K.D., Herzog, O. (2010) ‘An internet of things for transport logistics–an approach to connecting the information and material flows in autonomous cooperating logistics processes’, In: Proceedings of the 12th international MITIP conference on Information Technology & Innovation Processes of the Enterprises, pp. 54-67.
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  • Pang, Z., Chen, Q., Han, W., Zheng, L. (2015) ‘Value-centric design of the internet-of-things solution for food supply chain: value creation, sensor portfolio and information fusion’, Information Systems Frontiers, 17(2), pp.289-319.
  • Silva, R.F., Praça, I., Yoshizaki, H., Cugnasca, C.E. (2015) ‘Proposal of a traceability model for the raw Brazilian sugar supply chain using RFID and WSN’, In: Production and Operations Management Society, POMS 26th Annual Conference, Washington DC, USA.
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Feature Selection and Grouping of Cultivation Environment Data to Extract High/Low Yield Inhibition Factor of Soybeans
Katsuhiro Nagata, Midori Namba, Seiichi Ozawa, Yuya Chonan, Satoshi Hayashi, Takuji Nakamura, Hiroyuki Tsuji, Noriyuki Murakami, Ryo Nishide and Takenao Ohkawa
Pages: 96-101

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ABSTRACT: This work aims to extract high or low yield factors by analyzing soybean cultivation data and its cultivation environment. In this study, methods for using soybean data and investigating the cause of yield affected by the cultivation environment are proposed. As the soybean is affected by surrounding environment at each growing stage, the cultivation environment is also examined at each corresponding stage by dividing environment data. Qualitative values are generated at each stage, and the plants’ condition for each stage is simply expressed because the slight changes of the environment do not affect much the growth of soybean. Then, similar cultivation environments at each growing stage are grouped by clustering. In the grouping, features of cultivation environment are selected to eliminate groups that have the few number of soybean fields whose environments are similar, and features with low possibility to be classified as either the high and low yield factors are removed. Thus, a distinctive group that is inclined toward either high yield or low yield of soybean cultivation has been identified. The cultivation environment that may affect the yield of soybeans has been revealed.

References

  • Harel, K. , Fadida, H. , Slepoy, A. and Shilo, K.(2014) ‘The Effect of Mean Daily Tem-perature and Relative Humidity on Pollen, Fruit Set and Yield of Tomato Grown in Commercial Protected Cultivation’, Agronomy, Vol. 4, Issue. 1, pp. 167-177.
  • Japan Agricultural Development and Extension Association (JADEA) (2012) ‘Soybean Making for Improvement of Yield and Quality and Stable Production Q & A’, https://www.jadea.org/houkokusho/daizu/documents/daizu-kaitei.pdf, Accessed 26 April 2019 (in Japanese).
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  • Namba,M. , Umejima, K. , Nishide, R. , Ohkawa, T. , Ozawa, S. , Murakami, N. andTsuji, H.(2016) ‘Optimal Pattern Discovery based on Cultivation Data for Elucidation of High Yield Inhibition Factor of Soybean’, Proceedings of the 5th IIAE International Conference on Intelligent Systems and Image Processing, pp.209-216.
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MULTIPASS : Managing the Consents of Access to Farm Data in a Chain of Trust to Make New Services Emerge for Farmers
Bruno Lauga, Béatrice Balvay, Laurent Topart, Juliette Leclaire, Anthony Clenet, François Brun, François Pinet, Catherine Roussey and Mehdi Sine
Pages: 102-107

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ABSTRACT: With the emergence of digital technologies, farms become a relevant source of data to meet the challenges of multi-performance agriculture. Beyond the services provided, access to farmers' data depends on a clear understanding of their use, which must be done in a transparent way. Several codes of conduct at a national or international level push for a voluntary commitment to respect some good practices in the use of agricultural data. To provide a tool and answer farmer’s questions on the control of their data and the transparency of the data processing, the partners of the MULTIPASS project, have imagined an interoperable ecosystem of farmer consents management, protecting farmers from no consented uses of their data.Farmers’ expectations of such an ecosystem have been expressed during workshops. They want to better identify existing data flows, including actors, data processes, and data clusters. Based on the farmers’ expectations, the MULTIPASS project stakeholders have proposed the architecture of an ecosystem integrating two consent management tools as “pilots”. This ecosystem should take in charge the interoperability between each consent management tools or with future tools. This solution is based on a shared typology of data and data processes as well as on the specifications of the consent message content. All these elements should be easily accessible to meet the interoperability need of the ecosystem. It is also based on a router, which provides unified access to consent management tools (using API). In particular, it provides the farmer (beneficiary) with an exhaustive view of his/her consents (which can be distributed on several consent management systems), meeting farmers' expectations for transparency. It is also the point where a data provider can check whether the consent required to provide data exists, without needing to know which consent management system is concerned. In this project, the stakeholders want to demonstrate to agricultural professional organizations the benefits and feasibility of a consent management ecosystem. By strengthening the confidence of farmers to share data, the project will allow the emergence of new knowledge and new services.

References

  • Douville, T. (2019) ‘Contrat et données agricoles’, Droit rural, n° 469, 7p.
  • CNIL (2018), ‘Conformité RGPD : comment recueillir le consentement des personnes ?’ https://www.cnil.fr/fr/conformite-rgpd-comment-recueillir-le-consentement-des-personnes
  • GDPR(2016)‘General Data Protection RegulationEU2016/679, April27th,2016.
  • FNSEA and JA (2018) ‘DataAgri Charte sur l’utilisation des données agricoles’, 12p.
  • EU code of conduct (2018) ‘EU Code of conduct on agricultural data sharing by contractual agreement’, 20p.
  • Roussey, C., Chanet, J-P., Soulignac, V. and Bernard, S. (2011) ‘Les ontologies en agriculture’, journal‘Ingénierie des systèmes d’information (ISI)’, special number ‘Systèmes d'informations pour l'environnement’Vol 16, n°3, pp55-84.
  • Brun, F., Siné, M., Gallot, S., Lauga, B., Colinet, J., Cimino, M., Haezebrouck, T-P., Besnard, J., (2016) ‘ACTA -Les Instituts Techniques Agricoles: L’accès aux données pour la Recherche et l’Innovation en Agriculture. Position des Instituts Techniques Agricoles’.
  • FRANCE GENETIQUE ELEVAGE (2016) ‘Le consentement des éleveurs pour l’accès à leurs données -Comment le gérer ? Comment le faire respecter?’, note proposed by the Information Systems StrategyCommission.
  • Pinet, F., Roussey, C., Brun, T., Vigier, F (2009) ‘The use of UML as a tool for the formalisation of standards and the design of ontologies in agriculture’. Chapter in: Advances in Modeling Agricultural Systems, Springer, p.131-147.

Detecting of Approached Interaction with Cattle in Estrus Based on Community Transition and Cattle Distance
Shunta Fukumoto, Ryo Nishide, Yumi Takaki, Chikara Ohta, Kenji Oyama and Takenao Ohkawa
Pages: 108-113

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ABSTRACT: In stock breeding of beef cattle, it is essential to efficiently produce calves to maintain stable management. For this purpose, most farmers conduct artificial insemination, which should be performed after half a day of estrus detection. Therefore, in order to perform artificial insemination successfully, it is necessary to detect cattle in estrus precisely. Generally,searching the estrus is performed visually. Sometimes, a pedometer or a temperature sensor is used to grasp cattle's condition. These methods, however, do not consider the fact that the cattle live in a community with other cattle. Taking such cattle's sociality into account may enable to grasp the change in cattle's condition and detect cattle in estrus more accurately. This study focuses on the sociality of grazing cattle and aims to detect estrus by grasping the change of social behavior. Cattle in estrus tend to be approached by other cattle continuously for several hours. The behavior of approaching or being approached (stated as approaching-approached behavior from here on)is quantified by using the position information. In order to minimize the influence of momentum in quantification, we propose a method focusing on the direction of the cattle during movement. Moreover, we employed weight based on community history and distance between two cattle in order to reduce noise due to unintended approaching-approached behavior. Furthermore, we performed anomaly detection with a state-space model to detect cattle in estrus based on the quantification of the approaching-approached behavior in real-time. We verified the effectivity of the quantification for estrus detection with performed artificial inseminations. As a result, the precision was 0.579, the recall was 0.733 and F-measure was 0.647, and thus, confirmed the effectivity of our method.

References

  • Roelofs, J,B., EerdenburgF,J,C,M, van., Soede, N,M.andKemp, B. (2005) ‘Various behavioral signs of estrous and their relationship with time of ovulation in dairy cattle’, Theriogenology, 63, 5, pp. 1366–1377.
  • Blondel, V,D., Guillaume, J,L., LambiotteR.andLefebvre, E. (2008) ‘Fast Unfolding of Communities in Large Networks’,Journal of Statistical Mechanics,2008, pp. 10008.
  • Fukumoto, S., Nishide, R., Takaki, Y., Ohta, C., Oyama, K.and Ohkawa, T. (2018) ‘Quantifying the Approaching Behaviors for Interactions in Detecting Estrus of Breeding Cattle’,SoICT, pp. 235-242. doi: 10.1145/3287921.3287944.
  • Durbin, J.andKoopman, S,J.(2012) ‘Time Series Analysis by State Space Methods: Second Edition’, 38, Oxford University Press, Oxford

Blockchain: A Tool for Supply Chain Certification
Crescenzio Gallo, Nicola Faccilongo, Francesco Conto' and Nino Adamashvili
Pages: 114-119

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ABSTRACT: Blockchain technology allows to develop product certification processes, offering greater guarantees on the history of food, from the collection of raw materials to the flow between operators in the supply chain and up to the final consumer. This new technology can therefore represent a strategic element for the agro-food supply chains (and not only). On one hand, in order to defend production from climatic variability, prompt action to manage production factors can be taken, as well as to contain costs and minimize production and environmental risks. Additionally, to guarantee brand safety and protect specific territorial features from illegal competition with counterfeit products. In this work authors introduce the Blockchain technology and its usage and implications for the supply chain certification. Initially its main aspects are illustrated together with its historical origins; in the next sections the specific issues related to supply chain and the certification of products are examined, and then we propose an adoption scheme of blockchain technology for fostering a sound and effective certification of the production in the entire supply chain.

References

  • Aung, M. M., & Chang, Y.S. (2014). Traceability in a food supply chain: Safety and quality perspectives. Food Control 39, 172-184.
  • Hackett, R. (2017). Walmart and 9 Food Giants Team Up on IBM Blockchain Plans. Fortune.
  • Karikari, A., Zhu, L. & Dara, R. 2019, "Blockchain: The nextstep in the development of the Internet of Things",2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018, pp. 341.
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  • La Sala, P., FaccilongoN., & Fiore M. (2017). Integrated management of the PGI ‘Matera’ Bread chain. World Review of Entrepreneurship, Management and Sust. Development, 13 (5/6), 665-683.
  • Mei, Z. & Dinwoodie, J., (2005). Electronic shipping documentation in China's international supply chains. Supply Chain Management. Vol 10, 3, 198-205
  • Morabito, V. (2017). Business Innovation Through Blockchain.Cham: Springer International Publishing.
  • Mylrea, M. & Gourisetti, S.N.G., (2018). Blockchain for Supply Chain Cybersecurity, Optimization and Compliance. Proceedings -Resilience Week 2018, RWS 2018. Article number 8473517, 70-76
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  • Petek, I., & Zajec, N. (2018, April). Collaborative Intelligence and decentralized business community building–Potentials in Food/Nutrition sector. In2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW). IEEE.
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Decision

Definition of Reference Models for Functional Parameters and Price for Combine Harvesters
Tatevik Yezekyan, Giannantonio Armentano, Samuele Trestini, Luigi Sartori and Francesco Marinello
Pages: 120-125

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ABSTRACT: Operational and functional parameters of agricultural machines have essential importance and direct influence on farm fleet definition or optimisation (both for tractors and implements), machinery planning and management. Decision support systems and models have been developed in the past mainly analysing and quantifying farm costs while information lack existing correlation with functional parameters such as weight, power, efficiency, etc. Conversely, such parameters play an important role, not only with direct and indirect costs but also with agronomic and environmental performances. Such aspects are highly essential and to some extent critical for capital intensive machines such as combine harvesters.In the current research the functional parameters of combine harvesters have been analysed (power, weight and tank capacity) providing linear models for the variables which exhibit the highest predictive potential. Highest correlation exhibited power of the machine in a relation with price(r = 0.91)and tank capacity(r = 0.90), which allows to perform forecast analyses related to the evaluation and prediction of costs and performances, thus contributing to the optimisation of the fleet selection process and investments.

References

  • Bulgakov, V., Adamchuk, V., Arak, M., & Olt, J. (2015). Mathematical Modelling of the Process of Renewal of the Fleet of Combine Harvesters. Agriculture and Agricultural Science Procedia. https://doi.org/10.1016/j.aaspro.2015.12.027
  • Camarena, E. A., Gracia, C., & Cabrera Sixto, J. M. (2004). A Mixed Integer Linear Programming Machinery Selection Model for Multifarm Systems. Biosystems Engineering, 87(2), 145–154. https://doi.org/10.1016/j.biosystemseng.2003.10.003
  • De Baerdemaeker, J., & Saeys, W. (2013). Advanced control of combine harvesters. IFAC Proceedings Volumes (IFAC-PapersOnline). https://doi.org/10.3182/20130828-2-SF-3019.00069
  • Hermann, D. (2018). Optimisation of Combine Harvesters using Model-based Control. In DTU Elektro.
  • Kaspar, T. C., Colvin, T. S., Jaynes, D. B., Karlen, D. L., James, D. E., Meek, D. W., ... Butler, H. (2003). Relationship between six years of corn yields and terrain attributes. Precision Agriculture. https://doi.org/10.1023/A:1021867123125
  • Kortenbruck, D., Sapounas, A. A., Griepentrog, H. W., Paraforos, D. S., Ziogas, V., Vassiliadis, V., & Stamkopoulos, K. (2016). A Farm Management Information System Using Future Internet Technologies. IFAC-PapersOnLine. https://doi.org/10.1016/j.ifacol.2016.10.060
  • Peart, R. M., & Shoup, W. D. (2004). Agricultural Systems Management Ontimizing Efficiency and Performance. Marcel Dekker Inc., New York, pp. 280
  • Pezzuolo, A., Basso, B., Marinello, F., & Sartori, L. (2014). Using SALUS model for medium and long term simulations of energy efficiency in different tillage systems. Applied Mathematical Sciences. https://doi.org/10.12988/ams.2014.46447
  • Rodias, E., Berruto, R., Bochtis, D., Busato, P., & Sopegno, A. (2017). A computational tool for comparative energy cost analysis of multiple-crop production systems. Energies. https://doi.org/10.3390/en10070831
  • Søgaard, H. T., & Sørensen, C. G. (2004). A model for optimal selection of machinery sizes within the farm machinery system. Biosystems Engineering, 89(1), 13–28. https://doi.org/10.1016/j.biosystemseng.2004.05.004
  • Sopegno, A., Busato, P., Berruto, R., & Romanelli, T. L. (2016). A cost prediction model for machine operation in multi-field production systems. Scientia Agricola. https://doi.org/10.1590/0103-9016-2015-0304
  • Sørensen, C. A. G., Milan, M., Bochtis, D., Tieppo, R. C., & Romanelli, T. L. (2018). Modeling cost and energy demand in agricultural machinery fleets for soybean and maize cultivated using a no-tillage system. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2018.11.032
  • Vougioukas, S. G., Bochtis, D. D., Sørensen, C. G., Suomi, P., & Pesonen, L. (2011). Functional requirements for a future farm management information system. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2011.02.005
  • Yezekyan, T., Marinello, F., Armentano, G., & Sartori, L. (2018). Analysis of cost and performances of agricultural machinery: Reference model for sprayers. Agronomy Research. https://doi.org/10.15159/AR.18.049
  • Yezekyan, Tatevik, Marinello, F., Armentano, G., Trestini, S., & Sartori, L. (2018). Definition of Reference Models for Power, Weight, Working Width, and Price for Seeding Machines. Agriculture. https://doi.org/10.3390/agriculture8120186

Measurement of Income Risk as Benchmarking Tool for Dairy Farms
Gašper Petelin and Jaka Žgajnar
Pages: 126-131

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ABSTRACT: With increasing risks in agriculture, their management is becoming increasingly topical. Climate changes, price fluctuations and changes of institutional measures increase variability of farms’ income, which can have a negative effect. Effective risk management strategy can help farmers protecting themselves from such events and help stabilize their income. Given that we are talking about various factors that influence the risks, a multi step approach is required. First, layers of risks should be considered separately. Normal risks are usually managed on the farm with different costs strategies by farmers(OECD, 2011)while market and catastrophic risks are managed by the private sector or the state. Additionally, an effective risk measurement tool should be established. On top of that benchmarking could be an effective approach for increasing farm efficiency.The present paper therefore presents a conceptual approach to use the farm management information system PANTHEON Farming as a risk measurement and benchmarking tool. It is presented on the case of Slovenian dairy farms. First goal of such tool is to use farm level data, created through bookkeeping and accounting, to measure volatility of individual financial parameters. In such a manner,farmers would get a detailed overview of the income variability on his/her farm as well as an insight to what actually causes such variability. In the next step, tool would enable user to compare their results to benchmarks or to similar results of other farms. Farms that use PANTHEON Farming could share information about risk measurement and some additional details like production type, production intensity, location, etc. Such insight would enable farmers to compare (benchmark) their results and see, if their variability in some way stands out from benchmarks of sector or from farms in same location, intensity of production, feed usage, etc. These results would also indicate to the farmer some additional information like which type of production, feed or animal breed could help reduce income variability in similar condition. An important step of risk measurement is also time frame,selected for analysis, which can have a significant impact on results. Therefore,developed tool would enable farmer to choose either he wants to review only last year, last three years or Olympic average results which would properly recalculate the variability of each parameter. Such functionality as well as whole benchmarking tool could help farmers with their business. For farmers to be able to cope with increasing risks, adequate information should be provided as well as individual risk sources should be identified. This could also increase production optimization and risk management on farm level and could therefore increase resilience of farms to exposure risk. Policy makers could also benefit on the long run, as risk management on farm level would increase and could on the one hand minimize the need for policy interference with risk management and on the other minimize the exposure of individual farms to catastrophic risks.

References

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Numerical Determination of air Exchange Rate of and Inside a Naturally Ventilated Barn Depending on Incoming Wind Angle and Barn’s Length/Width
Moustapha Doumbia, Sabrina Hempel, David Janke and Thomas Amon
Pages: 132-137

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ABSTRACT: The air exchange rate is an important parameter in order to evaluate the gas emission of naturally ventilated barns. At the same time, understanding the flow inside such barns helps to investigate the comfort of the animals inside. Yet the air exchange rate is still difficult to assess mainly because of fluctuating influence stimuli such as wind (speed and angle), temperature, barn geometry etc. The main objective of the BELUVA project, financed by the German Research Foundation, is to systematically address those influences. The present study has been carried out in order to investigate the impact of incoming wind angle on the air exchange rate of animal occupied zones of barns with different length/width ratios.A numerical model with quadrilateral and polyhedral grid cells (hybrid mesh) was set up in ANSYS Workbench. The numerical domain was divided into blocks for better meshing controls and quality. The influence of the roughness height was studied as well to reduce the numerical error related to the non-horizontal homogeneity of the velocity profile downstream.Going from this numerical model, the air exchange rate of the whole barn and of the animal occupied zones inside has been determined and compared for different cases. The cases distinct them selves by three different incoming wind angles (0°, 45° and 90°),three different barn’s length (L) /barn’s width (W) (L/W=2,3,4) ratio and three different velocity magnitudes(1, 3 and 5ms-1). The results show that the influence of the velocity incident angle on the air exchange rate of the overall barn and the animal occupied zones inside can be classified in types at least for the 0° and 90° incident angles. This, depending on the L/W ratio, gives important information about the height of the local in air exchange rate.

References

  • Rong, L.(2001) ‘Mechanisms of natural ventilation in livestock buildings: Perspectives on past achievements and future challenges’, Biosystems engineering. doi: 10.1016/j.biosystemseng.2016.09.004.
  • Wu,W.(2012) ‘Evaluation of methods for determining air exchange rate in a naturally ventilated dairy cattle building with large openings using computational fluid dynamics (CFD), Atmospheric Environment,63,pp. 179 to 188. doi: 10.1016/j.atmosenv.2012.09.042.
  • Bjerg, B.(2013) ‘Modelling of ammonia emissions from naturally ventilated livestock buildings. Part 3: CFD modelling’, Biosystems engineering, 116,pp. 259 to 275. doi: 10.1016/j.biosystemseng.2013.06.012.
  • Norton,T. (2008) ‘Assessing the ventilation effectiveness of naturally ventilated livestock buildings under wind dominated conditions using computational fluid dynamics’, Biosystems engineering, 103, pp. 78 to 99. doi: 10.1016/j.biosystemseng.2009.02.007.
  • Fiedler, T. (2008) ‘Spatial distribution of air flow and CO2 concentration in a naturally ventilated dairy building’, Environmental Engineering and Management Journal, 13, 9, 2193-2200.
  • Blocken, B. (2017) ‘CFD simulation of the atmospheric boundary layer: wall function problems’, Atmospheric Environment. doi: 10.1016/j.atmosenv.2006.08.019.
  • Lanfrit, M. (2005) ‘Best practice guidelines for handling Automotive External Aerodynamics with FLUENT’.
  • ANSYS Fluent User's Guide, version 18.1 (2017)
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Dry Above ground Biomass Estimation for a Soybean Crop Using an Empirical Model in Greece
Christos Vamvakoulas, Stavros Alexandris and Ioannis Argyrokastritis
Pages: 138-143

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ABSTRACT: A new empirical equation for the estimation of daily Dry Above Ground Biomass (D-AGB) for a hybrid of soybean (Glycine max L.) is proposed. This equation requires data for three crop dependent parameters, Leaf Area Index, plant height and cumulative crop evapotranspiration. Bilinear surface regression analysis is used in order to estimate the factors entering the empirical model. For the calibration of the proposed model, yield data from a well-watered soybean crop for the year 2015, in the experimental field (0.1 ha) of the Agricultural University of Athens, are used as a reference. Verification of the validity of the model was obtained by using data from 2014 cultivation period for well-watered soybean cultivation (100% of crop evapotranspiration water treatment), as well as, data from three irrigation treatments (75%, 50%, 25% of crop evapotranspiration) for two cultivation periods (2014-2015). The proposed method for the estimation of D-AGBmay be proved as a useful tool for estimations without using destructive sampling.

References

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  • Alexandris, S., & Kerkides, P. (2003). New empirical formula for hourly estimations of reference evapotranspiration. Agric. Water Manage. 60, 181–198.
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  • Wicki, A., & Parlow, E. (2017). Attribution of local climate zones using a multitemporal land use/land cover classification scheme. J. Appl. Remote Sens., 11, 026001.
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Identification of Requirements for Implementing IOT on the Smart Beef Value Chain
Gustavo Marques Mostaço, Roberto Fray Silva and Carlos Eduardo Cugnasca
Pages: 144-149

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ABSTRACT: Smart services for livestock value chains can improve both productivity and sustainability by providing valuable information for the decision-making and management systems. By blending Internet of Things (IoT) technologies with Big Data analytics, it is possible to increase information and data exchange among supply chain agents, gain predictive insights in farming and marketing operations, drive real-time operational decisions and increase the efficiency of processes. This work provides an overview of the beef cattle VC comprising its stages, stakeholders, processes, and the informational flow. It also identifies the main requirements for implementing IoT on the beef cattle value chain, which are organized in 9 main services and 31 sub-services or activities. It sets the ground requirements for the future development of a framework for Smart Beef Cattle Services. These can also be used for developing a more general Smart Livestock Farming framework. Farm management research may benefit from the resulting requirements, using it to build services and architectures for a panorama of further automation and autonomous operations of the farms and agricultural supply chains.

References

  • Babinszky, L.,Halas, V. andVerstegen, M.W.A.(2011)‘Impacts of climate change on animal production and quality of animalfood products’,InTech.
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  • CEMA. (2017) ‘CEMA -European Agricultural Machinery -Innovative Livestock Technologies: Making Livestock Farming More Animal-Friendly, Sustainable & Competitive.’
  • Coleman, S. W. and Moore, J. E. (2003)‘’Feed quality and animal performance’.Field Crops Research,84(1-2), 17-29.
  • Dabbene, F., Gay, P.andTortia, C. (2014)‘Traceability issues in food supply chain management: A review’.Biosystems engineering,120, 65-80.
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  • HFAC. (2018) ‘Animal Care Standards: Beef Cattle’. Encyclopedia of Reproduction.
  • ITU-T. (2012)‘Y.2060 -Overview of the Internet of Things’. Recommendation ITU-T.
  • Mahmoud, R.,Yousuf, T.,Aloul, F.and Zualkernan, I.(2015) ‘Internet ofthings (IoT) security: Current status, challenges and prospective measures’,In: Internet Technology and Secured Transactions (ICITST), 2015 10th International Conference for. IEEE, pp. 336-341.
  • Sheridan, J. J. et al.(1991)‘Guidelines for slaughtering, meat cutting and further processing’. FAO.
  • Verdouw, C. N. et al.(2018) ‘A reference architecture for IoT-based logistic information systems in agri-food supply chains’.Enterprise information systems,12(7), 755-779.
  • Wolfert, S., Ge, L., Verdouw, C.andBogaardt, M. J. (2017) ‘Big data in smart farming –a review’.Agricultural Systems,153, 69-80.
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Comparison of the K-Means and Self-Organizing Maps Techniques to Label Agricultural Supply Chain Data
Roberto F. Silva, Gustavo M. Mostaço, Fernando Xavier, Antonio Mauro Saraiva and Carlos E. Cugnasca
Pages: 150-155

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ABSTRACT: The data produced in agricultural supply chains may be divided into three separated systems: (i) product identification and traceability, related to identifying production batches and places where the product has passed on the supply chain; (ii) environmental monitoring, considering mainly the temperature and relative humidity in storage and transportation; and (iii) processes, related to the data describing production processes and inputs used. Systems (i) and (ii) produce mainly structured data, while system (iii) produces non-structured data, and these are present in all agents in the supply chain. Data labeling on the different systems is an important step towards improving supply chain coordination and decision making related to traceability, production, and certification, among others. Nevertheless, it is a labor-intensive task, whose adoption is discouraged in data management activities. The main objective of this paper was to contribute to the reduction of interoperability problems by applying two clustering algorithms to label non-standardized data from agricultural supply chains. First, the data were clustered using k-means++ and self-organizing maps, with different model parameters. Then, a series of inferences were made to evaluate if the labels were correctly assigned, based on the characteristics of the data on each of the three systems. Lastly, a series of recommendations to improve the results of the models are discussed.

References

  • Arthur, D., Vassilvitskii, S. (2007) ‘K-means++: The advantages of careful seeding’, In: Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027-1035.
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  • Chopra, S., Meindl, P. (2013) ‘Supply chain management: Strategy, planning, and operation’, 5thed. New Jersey, USA: Pearson Education, 528pp.
  • Corella, V.P., Rosalen, R.C., Simarro, D.M. (2013) ‘SCIF-IRIS framework: a framework to facilitate interoperability in supply chains’. International Journal of Computer Integrated Manufacturing, 26(1-2), pp.67-86.
  • Harris, I., Wang, Y., Wang, H. (2015) ‘ICT in multimodal transport and technological trends: unleashing potential for the future’, International Journal of Production Economics, 159, pp.88-103.
  • Jain, A.K. (2010) ‘Data clustering: 50 years beyond K-means’, Pattern Recognition Letters, 31(8), pp.651-666.
  • Kohonen, T. (1982) ‘Self-organized formation of topologically correct feature maps’, Biological Cybernetics, 43(1), pp.59-69.
  • Omer, S.O., Abdalla, A.W.H., Mohammed, M.H., Singh, M. (2015) ‘Bayesian estimation of genotype-by-environment interaction in sorghum variety trials’. Communications in Biometry and Crop Science, 10, pp.82-95.
  • Pang, Z., Chen, Q., Han, W., Zheng, L. (2015) ‘Value-centric design of the internet-of-things solution for food supply chain: value creation, sensor portfolio and information fusion’, Information Systems Frontiers, 17(2), pp.289-319.
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4Crop Web Application for Monitoring Rain-Fed Crops in the Sahel
Tiziana De Filippis, Patrizio Vignaroli, Leandro Rocchi and Elena Rapisardi
Pages: 156-161

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ABSTRACT: Agro-geoinformation is the key information in the agricultural decision making and policy formulation process, especially in the countries where food security mainly depends on rain-fed crops production. It’s the case of Sudano-Sahel zone where scarce economic resources hamper regular monitoring of crops development; a context that requires new approaches to detect crops risk zones during the agricultural season. The advances of Earth observation and sensing technologies, as well as geo processing web tools, enable new opportunities and challenges in applying agro-geoinformation to crop monitoring and assessment. This paper presents the “4Crop” web application, an open source and interoperable solution for agricultural drought risk identification and forecasting in the Sahelian countries. The goal is the development of an operational tool that balances the lack of sufficient and timely acquisition of ground data using meteorological satellite open data sets. The whole web geoprocessing is based on the Crop RiskZone (CRZ) model. The model performs a soil water balance to evaluate the satisfaction of crop water requirements in each phenological stage of the growing period. The model also provides a qualitative evaluation of the expected crop yieldscompared with the potential one, taking into account both water stress intensity and the phenological stage of crops.The 4Crop web application currently running on Niger and Mali, and the outputs aim to identify installation and phenological phases of the main rain-fedcrops (millet, sorghum, groundnut, cow pea) and to create crop risk zones images for each selected country.The goal is to support Sudano-Sahel Early Warning Systems and any other local users in decision making and foster drought risk reduction and climate change resilience.The proposed approach aims to encourage the integration and sharing of interoperable and open source solutions and thus contribute to the setting-up of distributed climate services in developing countries.

References

  • Bacci M., Di Vecchia A., Genesio L., Tarchiani V., Vignaroli P. (2009a). Identification et Suivi des Zones à Risque agro-météorologique au Sahel. In Changements Globaux et Dèveloppement Durable en Afrique,volume 3. Rome, Aracne Editrice. ISBN 978-88-548-2980-0.
  • Hellmuth, M.E., Moorhead, A., Thomson, M.C., and Williams, J. (eds) 2007. Climate Risk Management in Africa: Learning from Practice. International Research Institute for Climate and Society (IRI), Columbia University, New York, USA.
  • Kleschenko A., Grom L., Ndiaye M., Stefanski R., 2004. The impact of agrometeorological applications for sustainable management of farming, forestry and livestock systems. Report CagM WMO/TD No. 1175, Geneva: World Meteorological Organization.
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  • Sultan B., Baron C., Dingkuhn M., Sarr B., Janicot S., 2005. La variabilité climatique en Afrique de l’Ouest aux échelles saisonnière et intra-saisonnière. II: applications à la sensibilité des rendements agricoles au Sahel. Sécheresse, 16(1):23-33.
  • Vignaroli, P., Rocchi, L., De Filippis, T., Tarchiani, V., Bacci, M., Toscano, P., Pasqui, M. and Rapisardi, E.: The Crop Risk Zones Monitoring System for resilience to drought in the Sahel. Geophysical Research Abstracts, Vol. 18, EGU2016-16616-3, 2016. EGU General Assembly, 2016.
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Water Use Efficiency (WUE) in a High Density Olive Grove: Variety, Planting Density and Irrigation Effects
Athanasios Gertsis and Gregory Yannakis
Pages: 162-167

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ABSTRACT: The new production systems in olives, with high (HD) and super high planting (SHD) densities, adapted fully to mechanical harvesting with row harvesters, represent a great challenge for all olive production countries. The available information is scarce and very localized, in terms of irrigation amount needed by the systems around the world. Great variations also exist, indicating that new approaches must be adapted to minimize water resources and increase the water use efficiency (WUE) in a tree species, which is a historical and cultural trademark in the Mediterranean areas and is rapidly expanding in all over the world. This study presents data from along-term high-densityolive grovein Greece, a unique study where all major agronomic inputs are evaluated (varieties, planting densities, irrigation and ground and foliar fertilization rates). Results in the past years revealed that there are many opportunities to increase irrigation water use efficiency (WUE) and achieve sustainable production. The study follows a holistic approach and precision agriculture technologies are applied to increase the overall sustainability value of these systems. The WUE of the super high densities were higher than the lower densities, in both varieties used (Koroneiki and Arbequina), while Koroneiki was higher than Arbequina in WUE and production. These trends were also shown in the past years, under different climate conditions, while 2018 year was adverse for olive production in Greece and in most Mediterranean areas.

References

  • Bacon, M. 2004. Water Use Efficiencyin Plant Biology.Oxford: Blackwell Publishing Ltd.
  • Gertsis, A., D. Fountas, I. Arpasanu and M. Michaloudis. 2013. Precision Agriculture applications in a high density olive grove adapted for mechanical harvesting in Greece. Procedia Technology 8 ( 2013 )152-156
  • Gertsis, A., K. Zoukidis and A. Mavridis. 2017. Evaluation of the water footprint and water use efficiency in a high density olive (Olea europeaL.) grove system. European Water Special Issue III-59. Paper No. 49. EWRA2017 -10th Word Congress on Water Resources and Environment). http://www.ewra.net/ew/issue_59.htm
  • Hijazi, A., M. Doghoze, N. Jouni, V. Nangia, M. Karrou, and T. Oweis. 2014. Water requirement and water-use efficiency for olive trees under different irrigation systems. 7th International Conf. on Water Resources in the Mediterranean Basin, Oct 10-12, Marrakech, Morocco.
  • Kijne, J.W., Barker, R., andMolden, D. (eds) 2003. Water Productivity in Agriculture: Limits and Opportunities for Improvement. Comprehensive Assessment of Water Management in Agriculture Series 1. CAB International, Wallingford, UK in association with International Water Management Institute (IWMI), Colombo.
  • Molden D, Oweis T, Steduto P , Bindraban P, Hanjra MA , Kijne J. 2010a. Improving agricultural water productivity: Between optimism and caution. Agricultural Water Management 97: 528–535
  • Nuberg, I. and I.Yunusa. 2003. Olive water use and yield –monitoring the relationship. RIRDC Publication No 03/048 RIRDC Project No UA-47A
  • Orgaz, F., Testi, L., Villalobos, F.J., Fereres, E. 2006. Water requirements of olive orchards II: determination of crop coefficients for irrigation scheduling. Irrigation Science, 24:77-84.
  • Subedi,Abhinaya and José L. Chávez. 2015. Crop Evapotranspiration (ET) Estimation Models: A Review and Discussion of the Applicability and Limitations of ET Methods. Journal of Agricultural Science; Vol. 7, No. 6; 2015.

Assessing the Efficiency of Arable Crops Production in a Cross-National Context
Spyros Niavis, Leonidas Sotirios Kyrgiakos, Christina Kleisiari and George Vlontzos
Pages: 168-173

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ABSTRACT: According to FAO, improving the efficiency of resources use for the production of agricultural products is a key prerequisite for achieving global agricultural sustainability. Thus, it is necessary to incorporate performance evaluations in order to assess sustainability from an academic standpoint in the context of supporting international agricultural policy such as the Common Agricultural Policy (CΑP). While efficiency is a major issue, there is a marked lack of bibliographic references focusing on agricultural efficiency issues on a global scale. However, much of the relevant international agricultural literature focuses on issues that concern individual countries or groups of countries with common characteristics. In the light of the above, this research seeks to evaluate the efficiency of national agricultural sectors in the production of arable crops internationally, a fact that will be achieved through Data Envelopment Analysis (DEA).

References

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  • Neumann, K., Verburg, P. H., Stehfest, E., & Müller, C. (2010). The yield gap of global grain production: A spatial analysis. Agricultural Systems, 103(5), 316–326. https://doi.org/10.1016/j.agsy.2010.02.004
  • Plaza-Bonilla, D., Nolot, J. M., Raffaillac, D., & Justes, E. (2017). Innovative cropping systems to reduce N inputs and maintain wheat yields by inserting grain legumes and cover crops in southwestern France. European Journal of Agronomy, 82, 331–341. https://doi.org/10.1016/j.eja.2016.05.010
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Economic Analysis of Unmanned Ground Vehicle Use in Conventional Agricultural Operations
Maria G. Lampridi, Dimitrios Kateris, Spyridon Tziakas and Dionysis Bochtis
Pages: 174-179

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ABSTRACT: With the rapidly developing technology of robotic vehicles and smart farming systems, conventional agricultural practices are evolving towards a new era of automation that promises to increase their efficiency and effectiveness contributing towards the need for increased production with lower economic and environmental costs. However, such technology is relatively new and most of the resulting products and services are used in an experimental stage. The scientific community and the industry mainly focus on the advancement of technology with the introduction of new smart farming products and services for which an extensive economic feasibility analysis has not yet been carried out. To that end, the aim of the paper is to perform an economic feasibility assessment of replacing conventional agricultural machinery and human labor with “smart” farming systems. The methodology used adopts the principles of conventional agricultural machinery cost calculation adjusting them to the use of Unmanned Ground Vehicles(UGV). On this basis, the cost of performing a conventional agricultural operation with the use of a robotic vehicle is estimated for a variety of different production scenarios. The scenarios are distinguished on the basis of the cultivation size and the application of different operation management schemes, as for example different charging times and the use of multiple vehicles to avoid the dead times caused by charging. The results highlight the effect of operation management in the overall efficiency of such systems which eventually affects the operation duration and the resulting cost, despite the fact that there are still many factors that need to be further investigated for the accurate cost estimation, e.g. repair and maintenance cost, salvage value.

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Intelligent Conversational Agent Integration to a Social Media Platform for Controlling IoT Devices in Smart Agriculture Facilities
Eleni Symeonaki, Konstantinos Arvanitis, Panagiotis Papageorgas and Dimitrios Piromalis
Pages: 180-185

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ABSTRACT: The issue of establishing interaction methods among users, applications and systems involved in Smart Agriculture through interfaces which are simple and friendly in end-usage is considered to be essential for achieving the maximum possible penetration of the IoT technologies in this sector, for the benefit of sustainability. Herewith, in this paper an attempt is made to encounter this issue through the involvement of intelligent conversational agent sin controlling IoT devices applied to Smart Agriculture facilities,by introducing the idea of developing a chat bot system which is integrated to a messenger application of a popular social media platform in natural language environment.This solution is considered to provide an efficient, effective and user-friendly mean of interaction between the end-users and the IoT devices deployed in agriculture facilities.

References

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Path Planning for Agricultural Vehicles Based on Generated Perception Maps
Dimitrios Katikaridis, Vasileios Moysiadis, Giorgos Vasileiadis, Damianos Kalaitzidis, Panagiotis Papazisis, Dimitrios Vrakas and Dionysis Bochtis
Pages: 186-191

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ABSTRACT: Agricultural field operations, such as spraying, harvesting and seeding, can potentially be automated and executed either by conventional machinery equipped with automation systems, or by autonomous machines. An automated operation must be planned and organized in a manner that, on one hand reduces risk and failure potential and, on the other hand, optimizes productivity and efficiency. However, the diversity of the natural out-door environment and the huge amount of diversified in type data required to picture the operational environment, comprise the hardest challenges for the deployment of fully automated agricultural operations.In the context of this paperan algorithmic approach was developed aiming at solving one of the various problems encountered in the autonomous agricultural operations. Specifically, the problem addressed is the navigation in the semi-structured environment of orchards. The navigation process consists of seeking a valid path connecting two predefined points in the field enabling the vehicle to travel between them. This is also a core functional component for robotic vehicles in agricultural operations.The developed software receives as input pre-processed data, a geotagged depiction of an orchard farm, which is obtained by an unmanned aerial vehicle. The pre-processing formats the coordinates which define the field's tracks. Based on this data, the software creates a grid-based map related to the accessible areas, utilized by a graph-based algorithm that produces the topological path planning solution. Subsequently, the solution is translated as a sequence of coordinates which define the produced optimal path.The software was executed, and its functionality validated in routing applications in an orchard using an autonomous farming vehicle.

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Integrated Information System for Robotics Applications in Agriculture
Ioannis Menexes, Vasileios Kolorizos, Christos Arvanitis, Georgios Banias, Dimitrios Kateris and Dionysis Bochtis
Pages: 192-197

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ABSTRACT: In the information era, focusing on the Agriculture modernization and introducing it to the new digital world, the most important elements of which are the online systems and cloud services, should be highly prioritized. Unfortunately it is not considered as vital when compared to the rest of the industry. Practices based on empirical observations and non-optimized methods for tasks, such as weed spraying, fertilizing, yield prediction or plant health monitoring, lead to inefficient resource use and provide undesired results with both economic and environmental negative impact. These are problems that could be alleviated with the help of the latest technological achievements. The developed information system presented is the framework that integrates all the different autonomously operating subsystems, ensuring bidirectional communication among them. State of the art Unmanned ground vehicles are combined with advanced hardware equipment and enable them to navigate autonomously inside fields. Laser-based sensors, that use the LIDAR technology, and Global Navigation Satellite System compatible devices, which provide centimeter-level accuracy, ensure that the robots are fully aware of their surroundings and location in the real world. Simultaneously, unmanned aerial vehicles fly above the work area and collect information that are input to the developed information system. Using the ground robots in the same framework with the unmanned aerial systems creates a network that consistently and reliably feeds the information system with data. This data is analyzed and stored on the cloud, in order to be compiled into applicable information and for future use.

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Mapping Agricultural Areas Using an Automated Unmanned Aerial Vehicle
Vasileios Moysiadis, Dimitrios Katikaridis, Ioannis Menexes, Dimitrios Vrakas, Aristotelis c. Tagarakis and Dionysis Bochtis
Pages: 198-203

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ABSTRACT: Mapping procedure consist an essential operation towards fully automated navigation within any operational environment.This paper focuses on the development of a system, as a first approach, for the one-way communication between an unmanned aerial vehicle (UAV)and an unmanned ground vehicle (UGV)for the detection and registration of operational environment entities and the extraction of the geographical coordinates as a basis information for the subsequent automated navigation of the ground vehicle. The case study took place in a commercial orchard installed with walnut trees located in Central Greece.As a mandatory scenario for the tree’s identification,was characterized the hypothesis of distinct, arranged circle like trees’ vegetation within the field.The developed system consist an inaugural process towards fully automated operations in agricultural fields. In addition, it contributes to the two-way communication between autonomous vehicles (UAV-UGV) focusing on collaborative predictions and actions for both pre-planned and real-time planning processes.

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A Concept Note on a Robotic System for Orchards Establishment
Giorgos Vasileiadis, Dimitrios Katikaridis, Vasileios Moysiadis, Dimitrios Kateris and Dionysis Bochtis
Pages: 204-209

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ABSTRACT: In the dawn of the Agriculture 4.0 era orchard establishment remains a manual and empirically executed task even though it affects the performance of the agricultural holding throughout its service life. The typical workflow involves a significant amount of manual labour, requiring teams of workers operating within a narrow window of availability. The narrow application time window is a major hindrance that is intensified by the decline of workforce availability in the developed countries. The work presented aims to alleviate these factors by introducing contemporary technological advancements. Furthermore, these features will be integrated in a fully autonomous framework. The compatibility and seamless integration of subsystems is insured by software, hardware and communication protocol choices. The system will be designed on available and widely used technologies, taking advantage of the open source community and contributing back to it the applied developed solutions. A central pillar of development will be the Robotic Operating System(ROS), ensuring compatibility and expandability of software and hardware components. Ubiquitous sensors can be used to control the system and add an increased level of unmanned operational capacity. On the mechanical design aspect of the system the framework for product development will be based on the functional design paradigm and will be grounded by the Quality Function Deployment framework. The resulting system is going to be designed to fit an array of Unmanned Ground Vehicles(UGV)and standardized conventional machinery. The operation is divided in stages and developed subsystems are going to be part of a modular architecture. Therefore, the system is flexible and adaptable to the diversified needs of spatially dispersed locations. Positive results are expected both on the pure operational performance, due to the automated nature of the system and on the future operations planning, due to the precise geo-referenced planting maps.

References

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Farm-to-Fork Traceability: Blockchain meets Agri-Food Supply Chain
Magdalena Stefanova, Michail Salampasis
Pages: 210-215

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ABSTRACT: The transfer of goods along the agri-food supply chain currently lacks sufficient visibility. Starting from providers of raw materials to farmers, to food processors, to distributors, to retail outlets and finally to the consumer the ability to trace the history, application or location of any food substance and entity has become an important priority. Consumer food decisions are not only affected by taste and price. Consumers are also concerned about transparency, traceability and social impact. They also expect all participants in the agri-food supply chain to have effective practices in place that allow for the rapid identification, location, and withdrawal of food lots when problems are suspected or confirmed. On top of consumer expectations, there are new regulations and standards that require an improved evaluation of foreign suppliers, fully documented processes and an assessment of all vulnerabilities in the supply chain. These social, economic and legislative demands as well as the fact that most existing traceability solutions are usually proprietary IT systems, make it difficult to develop a scalable, universal and cost-effective traceability system. Emerging technologies, such as blockchain could transform supply chain traceability as we know it and bring more transparency through the value chain, creating value to stakeholders. From a technology perspective, the proposed solution leverages blockchain to keep track of the flow of physical goods. This paper introduces FoodBlock, a theoretical, ‘farm-to-fork’ traceability solution, which integrates Hyperledger blockchain, mobile apps and GS1 identification standards. We have created a framework for building a minimal viable prototype by using existing technologies.

References

  • Liang, W., Jing, C. &Fan, J. (2015). Modeling and Implementation of Cattle/Beef Supply Chain Traceability Using a Distributed RFID-Based Framework in China.
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  • Salampasis, M., Tektonidis, D. & Kalogianni, E. (2012). TraceALL: a semantic web framework for food traceability systems. Journal of Systems and Information Technology, 14(4), pp. 302-317.
  • Stefanova, M. (2019). Precision Poultry Farming: Software Architecture Framework and Online Zootechnical Diary for Monitoring and Collaborating on Hens’ Health. Information and Communication Technologies in Modern Agricultural Development, pp. 191-205.
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Cross-Cutting Themes

Opportunities, Risks and Obstacles to the Implementation of Digitisation Technologies in German Agriculture
Jana Munz, Nicola Gindele and Reiner Doluschitz
Pages: 216-221

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ABSTRACT: Given that digitisation in agriculture is currently one of the most important ongoing developments in the agri-food sector in Germany, this paper presents the opportunities, risks and obstacles to imple-menting digitisation technologies on the German farm enterprise level. Based on an empirical survey with a response rate of 8.4% carried out at the beginning of 2018, 329 questionnaires on this topic were evaluated and analysed. A factor analysis was used to conceptualise20 variables regarding the individual opportunities, risks and obstacles to the implementation of digitisation technologies into 6 factors. It revealed positive effects of the use of digital systems, such as various economic and ecolog-ical advantages,as well as improved operational coordination. Risks of a socio-economic and financial nature could also be integrated. A lack of both knowledge and infrastructure continue to be major obstacles on the road to agriculture 4.0. Consequently,there is still a need for action in many areas to facilitate the implementation of digital systems in agriculture and to improve the functionality of the systems currently in use.

References

  • Bühl, Achim (Ed.) (2014) ‘SPSS 22. Einführung in die moderne Datenanalyse’, 14th editionPearson Deutschland: Pearson.
  • Deutscher Bauernverband (2016) ‘Landwirtschaft 4.0-Chancen und Handlungsbedarf’, Positionspapier des Präsidiums des Deutschen Bauernverbandes.Available online athttp://media.repro-mayr.de/34/661134.pdf.
  • Deutscher Bauernverband (2018) ‘Situationsbericht 17/18. 3. Agrarstruktur’. Available online athttp://www.bauernverband.de/33-betriebe-und-betriebsgroessen-803628, last accessedon29.03.2018
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  • El Bilali, Hamid; Allahyari, Mohammad Sadegh (2018) ‘Transition towards sustainability in agriculture and food systems: Role of informationand communication technologies’,Information Pro-cessing in Agriculture, 5 (4), pp. 456–464. doi: 10.1016/j.inpa.2018.06.006.
  • Griepentrog, Hans W. (2011) ‘Smart Farming: Praxisreife Lösungen, Erfordernisse und Techniken für morgen’, DLG Pressedienst 2011.Available online athttp://presse.dlg.org/pdf/dlg.org/1/4937.
  • Holster, Henri; Horakova, Sarka; Ipema, Bert; Fusai, Benedicte; Gannerini, Ginfranco; Martini, Daniel; Shalloo,Laurence;Schmid, Otto (2012) ‘Current situation on data exchange in agriculture in the EU27 & Switzerland. Final Report’.Available online athttp://li-brary.wur.nl/WebQuery/wurpubs/fulltext/206268.
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  • Lopotz, Harald (2013) ‘Precision Farming: Rechnen sich die Investitionen?’,Landwirtschaftskammer Nordrhein-Westfalen, 2013. Available online athttp://www.duesse.de/rueckblick/pdf/2013-06-19-rentabilitaet-pf.pdf, last accessedon28.04.2018.
  • Mintert, J.; Widmar, D.; Langemeier, M.; Boehlje, M.; Erickson, B. (2016) ‘The challenges of precision agriculture: is big data the answer?’,Paper presentedat the Southern Agricultural Economics Association (SAEA) Annaul Meeting; San Antonio, Texas; February 6-9, 2016. Available online athttps://ageconsearch.umn.edu/bitstream/230057/2/THE%20CHALLENGES%20OF%20PRECI-SION%20AGRICULTURE_manuscript_SAEA_2016.pdf.
  • Möller, Jens; Sonnen, Johannes (Ed.) (2016) ‘Datenmanagement in Landwirtschaft und Landtechnik. Intelligente Systeme-Stand derTechnik und neue Möglichkeiten’, Ruckelshausen, A.,et al.(Ed.):LectureNotes in Informatics(LNI), Gesellschaftfür Informatik,Bonn, pp. 133-136.Available onlineathttps://dl.gi.de/bitstream/handle/20.500.12116/788/133.pdf?sequence=1.
  • Rohleder, Bernhard; Krüsken, Bernhard (2016) ‘Digitalisierung in der Landwirtschaft’, Bitkom; Deutscher Bauernverband. Berlin, 2016. Available online athttps://www.bitkom-re-search.de/WebRoot/Store19/Shops/63742557/5819/BD75/5F7A/C381/3D6E/C0A8/2BBA/AC38/Digitalisierung_in_der_Landwirtschaft.pdf.
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  • Weltzien, Cornelia; Gebbers, Robin (2016) ‘Aktueller Stand der Technik im Bereich der Sensoren für Precision Agriculture’,Arno Ruckelshausenet. al(Ed.):GI-Edition Band 253 -Informatik in der Land-, Forst-und Ernährungswirtschaft, 36. GIL-Jahrestagung in February2016, Osnabrück. Bonn: Köllen. Available online athttp://www.gil-net.de/Publikationen/28_217.pdf.
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Options for Automatic Identifying of User Activities in Usability Testing
Jan Masner, Jiří Vaněk, Jan Pavlík, Eva Kánská and Edita Šilerová
Pages: 222-227

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ABSTRACT: When testing usability of applications, it is often needed to analyze behavior of users in terms of identifying their activities. The activity may be that the user is working on the assignment without problems, is searching for something, is absolutely lost in user interface, is filling a form, is studying the manual, etc. Identifying of the activities is usually done by tagging a video and audio record of the testing, optionally together with visualization of eye movements (eye tracking). It is a very time-consuming work for the usability experts. When testing in a specialized laboratory, we can obtain data from various measurements. Besides audio-visual record, data from eye-tracking, click tracking and keyboard tracking can be analyzed. Moreover, we can engage biometrical data such as pulse, skin temperature, humidity, hand movements, etc. The research question of the paper is whether it is possible to analyze all the data and develop methods and algorithms to automatically identify the user activities. We have performed several experiments in the specialized laboratory for usability testing. The paper describes the research design, experiments, methods of data processing and analysis.Finally, first conclusions and findings are discussed, as well as forthcoming research.The proposed research can be generalized to usability of any products.For example, using glasses for eye tracking and smartwatches, it is possible to conduct usability research of agricultural machinery.

References

  • Atterer, R., Wnuk, M., Schmidt, A., 2006. Knowing the user’s every move. https://doi.org/10.1145/1135777.1135811
  • Benda, P., Šimek, P., Masner, J., Vanek, J., 2017. Analysis of eAGRI web portal ergonomics and presentation of information in terms of the general public. Agris On-line Pap. Econ. Informatics 9. https://doi.org/10.7160/aol.2017.090401
  • Çakar, T., Rızvanoğlu, K., Öztürk, Ö., Çelik, D.Z., Gürvardar, İ., 2017. The use of neurometric and biometric research methods in understanding the user experience during product search of first-time buyers in E-commerce, in: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-319-58634-2_26
  • Harel, A., Kenett, R.S., Ruggeri, F., 2008. Decision support for user interface design: Usability diagnosis by time analysis of the user activity, in: Proceedings -International Computer Software and Applications Conference. https://doi.org/10.1109/COMPSAC.2008.61
  • Hild, J., Peinsipp-Byma, E., Klaus, E., 2012. Improving usability for video analysis using gaze-based interaction, in: Full Motion Video (FMV) Workflows and Technologies for Intelligence, Surveillance, and Reconnaissance (ISR) and Situational Awareness. https://doi.org/10.1117/12.919042
  • IDF, 2019. Usability [WWW Document]. URL https://www.interaction-design.org/literature/topics/usability
  • Jarolimek, J., Masner, J., Vanek, J., Pankova, L., 2019. Assessing Benefits of Precision Farmining Technologies in Sugar Beet Production. List. Cukrov. A Repar. 135, 57–63.
  • Kaikkonen, A., Kallio, T., Kankainen, A., Cankar, M., Kekalainen, A., 2005. Usability Testing of Mobile Applications: A Comparison between Laboratory and Field Testing. J. USABILITY Stud.
  • Mirza-Babaei, P., Long, S., Foley, E., McAllister, G., 2011. Understanding the contribution of biometrics to games user research. Proc. DiGRA.
  • Nørgaard, M., K, H., K, 2006. What do usability evaluators do in practice? An explorative study of think-aloud testing. In: Proceedings of the. DIS 2006 Conf. Des. Interact. Syst. ACM Press. New York. https://doi.org/10.1145/1142405.1142439
  • Petropavlovskiy, M., Nefedova, O., 2017. AUTOMATIZATION OF COLLECTION AND USE OF THE INFORMATION FROM OFFICIAL WEBSITES OF EDUCATIONAL ORGANIZATIONS. IJAEDU-Int. E-Journal Adv. Educ. https://doi.org/10.18768/ijaedu.336265
  • Prasse, M.J., 1990. Video analysis method. An integrated approach to usability assessment, in: Proceedings of the Human Factors Society. https://doi.org/10.1177/154193129003400436
  • Rubin, J., Chisnell, D., 2008. Handbook of Usability Testing, Indianapolis, IN: Wiley Pub. https://doi.org/10.1007/s13398-014-0173-7.2
  • Wang, J., Antonenko, P., Celepkolu, M., Jimenez, Y., Fieldman, E., Fieldman,A., 2019. Exploring Relationships Between Eye Tracking and Traditional Usability Testing Data. Int. J. Hum. Comput. Interact. https://doi.org/10.1080/10447318.2018.1464776
  • Wynn, J., Still, J.D., 2011. Motivating change and reducing cost with the discount video data analysis technique, in: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-642-21708-1_37

Spatial Analysis of Crop Diversity in Hungary Before and After Introduction of Greening
Márta Gaál and András Molnár
Pages: 228-233

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ABSTRACT: Due to the introduction of greening measures during the latest reform of the Common Agricultural Policy (CAP), the assessment of crop diversity has increasingly been a focus of interest. Better understanding of the factors that may influence the outcomes of diversity calculation could help the further evaluation of the regulations. This study investigates the spatial pattern of crop diversity in different scales and the changes over time in Hungary.Regional differences in crop diversity can be demonstrated at all scales examined, but the patterns are not exactly the same. Some increase in diversity can be revealed after the introduction of greening, but there are no further increasing tendencies.

References

  • Aguilar, J., Gramig, G.G., Hendrickson, J.R.,Archer, D.W.,Forcella, F.,Liebig, M.A. (2015) ‘Crop species diversity changes in the United States: 1978–2012’,PLoS ONE 10(8): e0136580. https://doi.org/10.1371/journal.pone.0136580
  • Donfouet, H.P.P., Barczak, A., Détang-Dessendre, C., Maigné, C. (2017)‘Crop Production and Crop Diversity in France: A Spatial Analysis’, Ecological Economics 134 pp. 29-39.http://dx.doi.org/10.1016/j.ecolecon.2016.11.016
  • EC (2017) ‘Evaluation study of the payment for agricultural practices beneficial for the climate and the environment’, https://ec.europa.eu/agriculture/sites/agriculture/files/fullrep_en.pdf
  • EU (2013)‘Regulation (EU) No 1307/2013 of the European Parliament and of the Council of 17 December 2013’,https://eur-lex.europa.eu/eli/reg/2013/1307/oj
  • Gaál,M., Blanco Mojica, L.F., Zubor-Nemes,A. (2018) ‘Spatio-temporal analysis of crop diversity in Hungary’,Proceedings of 60th Georgikon Scientific Conference, Keszthely. pp. 105-110.
  • Monteleone, M., Cammerini, A.R.B., Libutti, A. (2018)‘Agricultural “greening” and cropland diversification trends: Potential contribution of agroenergy crops in Capitanata (South Italy)’,Land Use Policy 70: 591-600. http://dx.doi.org/10.1016/j.landusepol.2017.10.038
  • Weigel, R., Koellner, T., Poppenborg, P., Bogner, C. (2018) ‘Crop diversity and stability of revenue on farms in Central Europe: An analysis of big data from a comprehensive agricultural census in Bavaria’, PLoS ONE 13(11): e0207454. https://doi.org/10.1371/journal.pone.0207454

Use of Electronic Identification and New Technologies on European Sheep Farms
Jean-Marc Gautier, Davies Claire Morgan, Tim W. J. Keady, Alan Bohan, Gilles Lagriffoul, Sezen Ocak, Ignacia Beltrán De Heredia, Antonello Carta, Dinu Gavojdian, Pauline Rivallant and Dominique Francois
Pages: 234-239

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ABSTRACT: Electronic identification of small ruminants is mandatory since 2010 in Europe. Associated with a context of widespread use of "connected" tools, the availability of solutions using new technologies to manage livestock and decrease workload,should become relevant for farmers. A survey was undertaken in the seven main EU sheep countries (France, Ireland, UK, Spain, Italy, Romania and Hungary) and Turkey (under two European projects: SheepNet and iSAGE) to determine the use of electronic identification (EID)associated technologies and barriers to the uptake of precision livestock farming (PLF)technologies. A total of 1,148 responses were collected and analysed. Sixty four percentof the respondents believe that EID and PLF are an opportunity for better flock/animal management but only 34% of them use it. This survey also highlight edthe type of technologies used and the main motivations and barriers for PLF uptake. To date, in the sheep sector, new technologies are mainly related to drafting,animal location, concentrate feed management and performance testing. This is the first study undertaken at EU level targeting the sheep sector. It identifies the main gaps to tackle and proposes some pathways in order to foster the use of new PLF technologies.

References

  • Banhazi, T., Dunn, M., Cook, P., Black, J., Durack, M. and Johnnson, I. (2007) ‘Development of precision livestock farming (PLF) technologies for the Australian pig industry’, In 3rdEuropean Precision Livestock farming Conference, 1, pp219-228.
  • Carpentier, L., Berckmans, D., Youssef, A., Berckmans, D., Van Waterschoot, T., Johnston, D., Ferguson, N., Earley, B., Fontana, I., Tullo, E., Guarino, M., Vranken, E., and Norton, T. (2018) ‘Automatic cough detection for bovine respiratory disease in a calf house’, Journal of Agricultural Engineering Research, 173, pp45-56.
  • Fernandez, A. P., Norton, T., Tullo, E., Van Hertem, T., Youssef, A., Exadaktylos, V., Vranken, E., Guarina, M. and Berckmans, D. (2018) ‘Real-time monitoring of broiler flock's welfare status using camera-based technology’, Journal of Agricultural Engineering Research, 173, pp 103-114.
  • Halachmi, I., Guarina, M., Bewley, J., and Pastell, M. (2019) ‘Smart animal agriculture: Application of real-time sensors to improve animal well-being and production’,Annual Review of Animal Biosciences, 7; In press.
  • Holtz, J., Gautier, J.M., Lefrileux, Y. and Caramelle-Holtz, E. (2016). ‘Use of electronic identification and new technologies in French goat farms’. IGC, Antalia, 26-28 septembre 2016.
  • Morgan-Davies, C.,Wilson, R., and Waterhouse, A. (2017) ‘Impacts of farmers' management styles on income and labour under alternative extensive land use scenarios’, Agricultural Systems, 155, pp 168-178.
  • Morgan-Davies, C., Lambe, N., Wishart, H., Waterhouse, A., Kenyon, F., McBean, D., and McCracken, D. (2018) ‘Impacts of using a precision livestock system targeted approach in mountain sheep flocks’, Livestock Science, 208, pp 67-76.
  • Pierpaoli, E., Carli, G., Pignatti, E., and Canavari, M. (2013) ‘Drivers of precision agriculture technologies adoption: A literature review’, Procedia Technology, 8, pp 61–69.
  • Ruiz-Garcia, L. and Lunadei, L. (2011) ‘The role of RFID in agriculture: applications, limitationsand challenges’, Computers and Electronics in Agriculture, 79, pp 42–50.

A Serious Video Game for Smart Farming Technologies
Spyros Fountas, Zisis Tsiropoulos, Panagiotis Stamatelopoulos, Evangelos Anastasiou, Tim Hutzenlaub, Mladen Radišić, Vladan Minic and , Patrick Rau
Pages: 240-245

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ABSTRACT: GATES is a near-to-market (Technology Readiness Level 7) serious game-based training platform that, through the use of a range of gaming technologies (3D scenarios, interactive storytelling, modelling and data), will train professionals and other stakeholders in the value chain in the use of Smart Farming Technology. Itis a cross-platform (Desktop/Mobile/Web) available for Android and Windows featuring online and offline synchronized modes. GATES features learning and behavioural triggers for stimulating players’ engagement, creativity and collaborative behaviours, while caring for gender and environmental issues related to the use of Smart Farming Technologies. In the current version, the player can choose between five crops (wheat, corn, potato, apples and vineyards) and can select one of multiple field operations, such as irrigation, fertilization, spraying and harvesting. The outcome of this serious game is a set of economic and environmental output of the selection of specific choices by the users. The gaming platform will be marketed as a white-label app, and will function either as a stand-alone or as a complementary tool to traditional training methods, covering a wide range of agricultural settings for the needs of different professionals in the Smart Farming Technologies value chain.

References

  • Barnes, A.P., Soto, I. Eory, V., Beck, B., Balafoutis, A., Sánchez, B., Vangeyte, J., Fountas, S., van der Wal, T. Gómez-Barbero, M., 2019. Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers. Land Use Policy, 80, 163-174.
  • CEMA, 2014. European machinery sales. http://www.cema-agri.org/publication/european-agricultural-machinery-sales-decline-5-2014
  • Fountas, S., Blackmore, S., Ess, D., Hawkins, S., Blumhoff, G., Lowenberg-Deboer, J., Sorensen, C. G., 2005.Farmer Experience with Precision Agriculture in Denmark and the US Eastern Corn Belt. Precision Agriculture, 6, 121-141.
  • Groff, J., Howells, C., Cranmer, S., 2010. The impact of console games in the classroom: Evidence from schools in Scotland. Futurelab, UK
  • JRC. Eds. Pablo J. Zarco-Tejada et al. 2014. Precision Agriculture –an opportunity for EU farmers, Potential support with the CAP 2014-2020.
  • Klopfer, E., Osterweil, S., Salen, K., 2009. Moving learning games forward. Cambridge, MA: The Education Arcade.

Innovative Agribusiness in Greece
Avraam Mavridis, Maro Vlachopoulou and Athanasios Gertsis
Pages: 246-251

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ABSTRACT: The heterogeneous framework of activities in the agricultural sector requires efficient, dynamic and holistic approaches in fair and equitable ways of management, cultivation practices and education, research, trade and policy strategies. Such demanding, interdisciplinary applications can be operated extensively today, more than ever before through Exploitation of Informatics. Such activities require a secure, yet powerful framework of hardware and software facilities in order to unlock the potentials of innovative agribusinesses for all agricultural products’ value chain parties and end-users, while aiming to support and develop benefits of agro-environmental sustainability. Latest accomplishments in Earth Observation technologies, Geoinformatics, AI (Artificial Intelligence), IoT (Internet of Things), Smart Farming Sensors, Precision Agriculture, Digital Agriculture, Cloud Services and Modular Agricultural Robotics constitute a fruitful region of continuous development. Current approaches worldwide are trying to develop new agricultural curriculum developing the educational background of the scientist who will support such activities: the data agronomist. Additionally, new digital skills are required to be developed by all actors participating in this framework: the farmers, the agronomist, the researcher, the academics, the consumers, the entrepreneurs and the policy makers. This paper will approach and present the level of exploitation and incorporation of different Information Technologies in the Agricultural Sector of Greece towards the development of innovative agribusiness opportunities, regardless of the gender, age, or/and educational background. Such innovative agribusiness potentials will be based on current available data repositories and web-based networking, while providing elements of further advancements in the near future, so as to combat restraints of economic and environmental crisis in the country.

References

  • Alexandratos, N. & Bruinsma, J. (2012). ‘World agriculture towards 2030/2050: the 2012 revision’. ESA Working Paper No. 12–03. Rome, FAO(Food and Agriculture Organization).
  • Boehlje, M., Roucan-Kane, M., & Bröring, S. (2011). ‘Future agribusiness challenges: Strategic uncertainty, innovation and structural change’.International Food and Agribusiness Management Review,14(5), 53-82.
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