CIOSTA 2019
XXXVIII CIOSTA & CIGR V International Conference
Rhodes, Greece, 24-26 June, 2019

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Production

Agroecological Efficiency of Site-Specific Fertiliser Application Systems in the North-West of the Russian Federation
Nadezhda Tcyganova, Aleksei Ivanov
Pages: 12-16

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ABSTRACT :The high spatial soil variability is the natural feature of soil covering in the North-West region of the Russian Federation. This heterogeneity requires site-specific management at the field scale. The main objective of the presented research is to examine the effectiveness of site-specificfertiliser application by reference of specific features of the agromicro-landscape conditions. The field site is a 22-ha field located in Leningrad region. The soil was typical and gleyic sod-podzolic. The most important characteristic for the present research is the high spatial variability of soil properties. The introduced crop rotation was: potato –spring barley –perennial grasses of the first and second harvest year –winter wheat. Research factors were (1) control (no fertilisation); CvF -conventional fertiliser application: mineral fertilisers added annually according to conventional agriculture practice; SsF I -mineral fertilisers added annually according to soil properties; SsF II -in 2008 only potassium fertilisers were applied site-specificallyto reduce spatial variability of potassium content. During the following years, the after-effect was observed and mineral fertilisers were applied uniformly; SsF III –mineral fertilisers added annually according to yield map data; (2) agromicro-landscapes (accumulative-eluvial, accumulative, eluvial and transite-accumulative). The average data of the five-year investigation show that the highest productivity without fertilisation was gained in the accumulative agromicro-landscape, and the lowest –in the eluvial agromicro-landscape. The highest yield of all investigated crops was harvested when potassium fertilisers had been applied site-specifically. In other cases, the yields were almost equal in conventional and site-specificfertilisation.

References

  • Bechar, A. and Vigneault, C. (2016) ‘Agricultural robots for field operations: Concepts and components’, Biosystems Engineering, 149, pp. 94–111. doi: 10.1016/j.biosystemseng.2016.06.014
  • Fowler, D., Coyle, M., Skiba, U., Sutton, M. A., Cape, J. N., Reis, S., et al. (2013)‘The global nitrogen cycle in the twenty-first century’, Philosophical Transactions of the Royal Society B: Biological Science, 368(20130164), pp. 1–13
  • Galloway, J. N., Aber, J. D., Erisman, J. W., Seitzinger, S. P., Howarth, R. W., Cowling, E. B., et al. (2003) ‘The nitrogen cascade‘,BioScience,53(4), pp. 341–356
  • Khanna, M. (2001)‘Sequential adoption of site-specific technologies and its implication for nitrogen productivity: A double selectivity model‘,American Journal of Agricultural Economics,83, pp/ 35 –51
  • Lechner, W. andBaumann, S. (2000)‘Global navigation satellite systems‘,Computers and Electronics in Agriculture,25, pp. 67 –85
  • Liu, J., You, L., Amini, M., Obersteiner, M., Herrero, M., Zehnder, A. J. B., et al. (2010)‘A high-resolution assessment on global nitrogen flows in cropland‘,Proceedings of the National Academy of Science of United States of America,107(17), pp. 8035–8040
  • Roberts, R.K., English, D.C., Larson, J.A., Cochran, R.L., Goodman, W.R., Larkin, S.L., et al. (2004)‘Adoption of site-specific information and variable-rate technologies in cotton precision farming‘,Journal of Agricultural and Applied Economics,30, pp. 143 –158

Effect of Extruded Plantain Peel-Based Fish Feed Diet on Growth Performance and Nutrient Utilization of Catfish (Clarias Gariepinus)
Oduntan O.B., Oduntan A.O. and Ogunmokun R.O.
Pages: 17-22

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ABSTRACT: The increasing cost of fish feed production has led to the need to search for alternative and non-conventional raw materials, including waste. Indiscriminate discarding of plantain peel has led to environmental challenges. A possible way to manage this waste could be to produce fish feed through extrusion cooking. Plantain peel flour produced by drying and grinding fresh peel obtained from a processing plant and was used to replace wheat bran at different levels (0, 5, 10, 15 and 20%) to produce a formulated balanced diet fish feed. The extruded feed was used daily at 5% body weight for eight weeks for the feeding trial. Growth performance parameters (Total Weight Gain (TWG) and Specific Growth Rate (SGR))and nutrient use indices(Feed Conversion Ratio (FCR)andFeed Efficiency (FE))were assessed. Data were analyzed using ANOVA at α0.05, for the sensitivity analysis. The formulated fish feed with 15% plantain peel showed the highest weight gain and best FCR. For the growth parameters, a significant variation of p <0.05 was observed. Fish fed with 5% and 15%% plantain peel had the highest survival ratio. Significant variation was observed in the apparent digestibility of the control and experimental feed. Plantain peel could be used to formulate extruded fish feed to reduce production cost and environmental nuisance.

References

  • Anuonye, J.C., Onuh, J.O., Evansegwim, E. and Adeyemo, S. O. (2010) ‘Nutrient and antinutrient composition of extruded acha/soybean blends’,Journal of Food Processing and Preservation, 34, pp.680–691.doi.org/10.1111/j.1745-4549.2009.00425.x.
  • Ajasin, F. O., Omole, A. J., Oluokun, J. A., Obi, O. O. and Owosibo A. (2004) ‘Performance characteristics of weaned rabbits fed plantain peel as replacement maize’,WorldJournal of Zoology,1,(1) pp. 30-32.doi:10.22161/ijaers/3.10.26.
  • Aderolu, A.Z.and Oyedokun, G. (2009)‘Comparative utilization ofbiodegraded and undegraded rice husk in Clarias gariepinus diet’, African Journal of Biotechnlogy, 8(7), pp. 1358-1362. doi: 10.3923/jfas.2008.312.319.
  • Agbabiaka, L.A., Okoeie, K.C., Ezeafulukwe, C.F. (2013) ‘Plantain peels as dietary supplement in practical diets for African catfish (Clarias gariepinus burchell 1822) fingerlings’,Agriculture and Biology Journal of North America, 4(2), pp. 155 –159. doi:10.5251/abjna.2013.4.2.155.159.
  • AOAC (2005) ‘Association of Official Analytical Chemists’,Official Methods of Analysis, 18th edn (edited by W. Horwitz and G.W. Latimer) Gathersburg, MD, USA: AOAC International.
  • Lawal, M.O., Aderolu, A.Z., Dosunmu, F.R. Aarode, O.O. (2014) ‘Dietary effects of ripe and unripe Banana peels on the growth and economy of production of juvenile catfish (Clarias gariepinus Burchell, 1822)’,Journal of FisheriesSciences.com, 8(3), pp. 220 -227. doi:10.3153/jfscom.201428.
  • Lemos, M.V.A., Arantes, T.Q., Sonto, C.N., Martins, P. G.P., Araujo, J. G., Guimaraes, I. G. (2014)‘Effects of digestible protein to energy ratios on growth and carcass chemical composition of Siamese fighting fish’, Betta splendens, 38(1), pp. 76-84.doi: org/10.1590/S1413-70542014000100009.
  • Morais, S. G., Bell, J. G., Robertson, D. A., Roy, W. J., Morris, P. C. (2001) ‘Protein/Lipids ratios in extruded diets for Atlantic (Gadus morhua L.) effects on growth, feed utilization, muscle composition and liver histology’, Aquaculture, 203, (1-2) pp. 101 –119. doi: 10.1016/S0044-8486(01) 00618-4.
  • Oujifard,A., Seyfabadi, J., Kenari, A. A., Rezaei, M. (2012) ‘Fish meal replacement with rice protein concentrate in a practical diet for the Pacific white shrimp, Litopenaeus vannamei Boone, 1931’, Aquaculture International, 20, pp. 117 –129. doi: 10.1007/s10499-011-9446-8.
  • Wolfe, K., Wu, X., and Liu, R. H. (2003)‘Antioxidant activity of apple peels’,Journal of Agricultural and Food Chemistry, 51,pp. 609-614.doi: 10.1021/jf020782a.
  • Singh, S., Gamlath, S., Wakeling, L. (2007). Nutritional aspects of food extrusion: A review. International Journal of Food Science and Technology. 42(8): 916 –929.doi:10.1111/j.1365-2621.2006.01309.x.
  • Smith, M. D., Roheim, C. A., Crowder, L. B., Halpern, B. S., Turnipseed, M., Anderson, J. L., Asche, F., Bourillón, L., Guttormsen, A. G., Kahn, A., Liguori, L. A., McNevin, A., O’Connor, M., Squires, D., Tyedemers, P., Brownstein, C., Carden, K., Klinger, D. H., Sagarin, R. and Selkoe, K. A. (2010). “Sustainability and Global Seafood.” Science, 327, pp. 784–786. doi: 10.1126/science.1185345.

Effect of Extrusion Conditions on the Throughput of Extruder for the Production of Pineapple Pomace Based Fish Feed
Oduntan O.B. and Bamgboye A.I.
Pages: 23-28

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ABSTRACT: Pineapple pomace based extrudates could serve as fish feed with significant health benefits to the fish if supplemented with adequate amountsof other ingredients and mineral-vitamin blends. Pomace is generally disposed during production in juice processing industries and has valuable use in fish production. Response surface methodology was used to examine the extrusion processing effect parameters such as feeding rate (1.28, 1.44 and 1.60 kg min-1), screw speed (305, 355 and 405 rpm), barrel temperature (60, 80 , 100 and 120°C) cutting speed (1300, 1400 and 1500 rpm) and open die hole (50, 75 and 100%) in relation to moisture content of the mash (16, 19 and 22%) with increased pomace inclusion (5-20%) on the throughput of a single screw extruder. Throughput significantly decreased with increased inclusion of pineapple pomace, moisture content, die cutting speed, open surface hole and reduced screw speed. The extruder worked optimally to achieve a throughput of 46.82 kghr-1at feeding speed (1.60 kg min-1), screw speed (405 rpm), moisture (16.0%), temperature (120°C), cutting speed (1400 rpm), pomace inclusion (5.0%) and open surface (100%). The use of pineapple pomace to produce fish feed and extruder performance is a novel approach with potential to reduce environmental nuisance.

References

  • Bereaux, Y., Charmeau, J. Y.and Moguedet, M. (2009) ‘Asimple model of throughput and pressuredevelopment for single screw’, Journal of materials processing technology, 209, pp. 611–618. doi:10.1016/j.jmatprotec.2008.02.070
  • Bennich, T. and Belyazid, S. (2017) ‘The Route to Sustainability—Prospects and Challenges of the Bio-Based Economy’Sustainability,9(6), pp. 887-904.doi.org/10.3390/su9060887
  • Chevanan, N., Rosentrater, K. A. and Muthukumarappan, K. (2008) ‘Effect of processing conditions on feed ingredients containing DDGS in single screw extrusion’, Food Bioprocess Technology Journal of Cereal Chemistry, 3, pp. 111-120.doi: 10.1021/jf9709562.
  • Devi, L. K., Karoulia, S. and Chaudhary, N. (2016) ‘Preparation of High Dietary Fibre Cookies from Pineapple (Ananas comosus) Pomace’. International Journal of Science and Research5(5), pp. 1368-1372.doi10.1007/s11947-008-0065-y.
  • Fallahi, P., Rosentrater, K. A Muthukumarappan, K. and Tulbek, M. (2013) ‘Effects of steam, moisture, and screw speed on physical properties of DDGS-based extrudate’, Cereal Chemistry, 90, pp. 186–197.doi.10.1094/CCHEM-08-12-0102-R.
  • Ηeuze, V., Tran, G. and Giger-Reverdin, S.(2013), ‘Pineapple by-products’Feedipedia.org. A programme by INRA, CIRAD, AFZ and FAO. http://www.feedipedia.org/node/676,16:46
  • Hosseini Ghaboos, S. H., Ardabili, S. M. S., Kashaninejad, M., Asadi, G. and Aalami, M. (2016) ‘Combined infrared-vacuum drying of pumpkin slices’ Journal of Food Science and Technology 5 pp. 1-9.doi:10.1007/s13197-016-2212-1
  • Lundblad, K. K., Hancock, J. D., Behnke, K. C., McKinney, L. J., Alavi, S., Prestløkken, E. and Sørensen, M. (2012) ‘Ileal digestibility of crude protein, amino acids, dry matter and phosphorous in pigs fed diets steam conditioned at low and high temperature, expander conditioned or extruder processed’, Animal Feed Science and Technology, 172, pp. 237-241. doi: 10.1016/j.anifeedsci.2011.12.025
  • Njieassam, E. S. (2016) ‘Effects of using Blood Meal on the Growth and Mortality of Catfish. Journal of Ecosystem and Ecography’ 6 pp. 204. doi:10.4172/2157-7625.1000204.
  • Oduntan, O. B., and Bamgboye, A. I. (2015) ‘Optimization of extrusion point pressure of pineapple pomace based mash’ Agricultural Engineering Int: CIGR Journal,17 (2), pp. 151-159.doi:10.17221/77/2012-RAE.
  • Oduntan, O. B., Koya, O. A. and Faborode, M. O. (2014), ‘Design, fabrication and testing of a cassava pelletizer. Res. Agr. Eng., 60,pp. 148–154.doi:10.17221/77/2012-RAE.
  • Pardeshi IL and Chattopadhyay P. K. (2014) ‘Whirling bed hot air puffing kinetics of rice-soy ready-to-eat (RTE) snacks’ Journal of Ready to Eat Foods, 1(1), pp. 1-10.
  • Rosentrater, K. A., Murthukumarappah, K. and Kannadhason, S. (2009) ‘Effect of ingredients and extrusion parameters on properties of aqua feeds containing DDGS and corn starch’ Journal of Aquaculture Feed Science and Nutrition 1 (2), pp. 44-60.doi: 10.1016/0260-8774(87)90035-5.
  • Sacilik, K, and G. Unal. (2005) ‘Dehydration characteristics of Kastamonu garlic slices’ Bio-system Engineering, 92 (2), pp. 207-215.doi:10.1016/j.biosystemseng.2005.06.006.
  • Smith, M. D., Roheim, C. A., Crowder, L. B., Halpern, B. S., Turnipseed, M., Anderson, J. L., Asche, F., Bourillón, L., Guttormsen, A. G., Kahn, A., Liguori, L. A., McNevin, A., O’Connor, M., Squires, D., Tyedemers, P., Brownstein, C., Carden, K., Klinger, D. H., Sagarin, R. and Selkoe, K. A. (2010). “Sustainability and Global Seafood.” Science, 327, pp. 784–786.doi: 10.1126/science.1185345.
  • Tiwari, A. and Jha S. K. (2017) ‘Extrusion Cooking Technology: Principal Mechanism and Effect on Direct expanded snacks -an overview’, international journal of food studies. 6, pp. 113-128.doi:10.7455/ijfs/6.1.2017.a10.

Prediction of the Local Air Exchange Rate in Animal Occupied Zones of a Naturally Ventilated Barn
Moustapha Doumbia, Sabrina Hempel, David Janke and Thomas Amon
Pages: 29-34

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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 evaluate the comfort of the animals inside. In this context, the local air exchange rate in subvolumes of the barn (i.e. individual animal occupied zones) is far more interesting than the total air exchange rate. Yet even the total air exchange rate is difficult to assess mainly because of fluctuating influence stimuli such as wind (speed and direction), temperature, barn geometry etc. One objective of the BELUVA project, financed by the German Research Foundation, is to address those influences and derive a parametric function for local air exchange rates. The functionwill further permit to answer questions associated with precision livestock farming. For example, for a given length/width ratio of a barn and a given inflow speed and angle, in which animal occupied zones a supporting mechanical ventilation must be switch on. The present study has been carried out in order to evaluate the impact of incidentwind angle and barn’s length/width ratio on the local air exchange rate in animal occupied zones of barns. Beforehand the numerical model has been validated with measurements done inside a boundary layer wind tunnelwith a down sized 1/100 barn. Threedifferent incidentwind angles (0°, 45° and 90°) and threedifferentratios of barn’s length (L) /barn’s width (W) (L/W=2,3,4) have been considered. The results of this simplified model show that, while the barn’s overall air exchange rate is independent of the length to width ratio, the onesfor animaloccupied zones inside the barn arenot. The local air exchange rate depends strongly on the position inside the barn and the velocity incident angle.A model extension towards a full-scale building with surroundings and including the effects of animals as obstacles and heatsources is on-going in order to further increase the accuracy of the predicted local air exchange rates.

References

  • Saha, C.K. (2014) ‘Assessing effects of different opening combinations on airflow pattern and air exchange rate of a naturally ventilated dairy building’, Proceedings International Conference of Agricultural Engineering, Zurich, 06-10.07.2014.
  • Qianying, Y. (2017) ‘Wind Tunnel Investigations of Sidewall Opening Effects on Indoor Airflows of a Cross-Ventilated Dairy Building’, Energy & Buildings. doi: 10.1016/j.enbuild.2018.07.026
  • Gebremedhin, K.G.(2004) ‘Simulation of flow field of a ventilated and occupied animal space with different inlet and outlet conditions’, Journal of Thermal Biology. doi: 10.1016/j.jtherbio.2004.10.001.
  • Durbin, P.A. (2001) ‘Statistical Theory and Modelling for Turbulent Flows’, John Wiley &Sons. ISBN 0-471-49744-4.
  • 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’.
  • Statisches Jahrbuch über Ernährung, Landwirtschaft und Forsten der Bundesrepublik Deutschland(2017)

Machine Learning Algorithms Comparison for Image Classification on Anthracnose Infected Walnut Tree Canopies
Athanasios Anagnostis, Gavriela Asiminari, Georgios Dolias, Christos Arvanitis, Elpiniki Papageorgiou, Charalambos Myresiotis and Dionysis Bochtis
Pages: 35-40

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ABSTRACT: Fungal diseases such as anthracnose, can be catastrophic to crops worldwide because it can destructively damage the canopies of trees and can also spread easily to nearby trees. Copper spaying, adequate pruning and proper sanitation, renders the treatment of such diseases as easy, however, the main concern in such cases is the spreading prevention by early detection systems. This can be dealt with automated procedures offered in precision agriculture such as automatic image collection and real-time classification by smart systems. Purpose of this study isto compare the most famous ML algorithms for classification, in orderto investigate the applicability and effectiveness of an image-based classifier on anthracnose infected canopies. Various machine learning algorithmswere employed, tested,evaluatedandcompared based ontheirabilities and limitations.Thecomparison is conducted based on several performance metrics and finally, the applicability of the best performing architectureis discussed for real-life applications.

References

  • Altman, N. S. (1992) ‘An introduction to kernel and nearest-neighbor nonparametric regression’, American Statistician. doi: 10.1080/00031305.1992.10475879.
  • Amara, J., Bouaziz, B. and Algergawy, A. (2017) ‘A Deep Learning-based Approach for Banana Leaf Diseases Classification’, in BTW.
  • Anand, R., Veni, S. and Aravinth, J. (2016) ‘An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method’, in 2016 International Conference on Recent Trends in Information Technology, ICRTIT 2016. doi: 10.1109/ICRTIT.2016.7569531.
  • Bharate, A. A. and Shirdhonkar, M. S. (2018) ‘A review on plant disease detection using image processing’, in Proceedings of the International Conference on Intelligent Sustainable Systems, ICISS 2017, pp. 103–109. doi:10.1109/ISS1.2017.8389326.
  • Breiman, L. (1984) ‘Classification and regression trees Regression trees’, Encyclopedia of Ecology. doi: 10.1007/s00038-011-0315-z.
  • Breiman, L. (2001) ‘Random Forrest’, Machine Learning. doi: 10.1023/A:1010933404324.
  • Büyüköztürk, Ş. and Çokluk-Bökeoǧlu, Ö. (2008) ‘Discriminant function analysis: Concept and application’, Egitim Arastirmalari -Eurasian Journal of Educational Research, (33), pp. 73–92.
  • Ferentinos, K. P. (2018) ‘Deep learning models for plant disease detection and diagnosis’, Computers and Electronics in Agriculture. doi: 10.1016/j.compag.2018.01.009.
  • Freund, Y. and Schapire, R. R. E. (1996) ‘Experiments with a New Boosting Algorithm’, International Conference on Machine Learning. doi: 10.1.1.133.1040.
  • Fuentes, A. et al.(2017) ‘A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition’, Sensors (Switzerland). doi: 10.3390/s17092022.
  • Gamal, A. et al.(2017) ‘A New Proposed Model for Plant Diseases Monitoring Based on Data Mining Techniques’, in Hakeem, K. R. et al. (eds) Plant Bioinformatics: Decoding the Phyta. Cham: Springer International Publishing, pp. 179–195. doi: 10.1007/978-3-319-67156-7_6.
  • Liakos, K. G. et al.(2018) ‘Machine learning in agriculture: A review’, Sensors (Switzerland). Multidisciplinary Digital Publishing Institute, p. 2674. doi: 10.3390/s18082674.
  • Liu, B. et al.(2018) ‘Identification of apple leaf diseases based on deep convolutional neural networks’, Symmetry. doi: 10.3390/sym10010011.
  • Mason, L. et al.(1999) ‘Boosting algorithms as gradient descent in Function space’, Nips. doi: 10.1109/5.58323.
  • McCulloch, W. S. and Pitts, W. (1943) ‘A logical calculus of the ideas immanent in nervous activity’, The Bulletin of Mathematical Biophysics. doi: 10.1007/BF02478259.
  • Mg, A. et al.(2017) ‘Plant Leaf Disease Detection using Deep Learning and Convolutional Neural Network’, International Journal of Engineering Science and Computing.
  • Mohanty, S. P., Hughes, D. P. and Salathé, M. (2016) ‘Using Deep Learning for Image-Based Plant Disease Detection’, Frontiers in Plant Science. doi: 10.3389/fpls.2016.01419.
  • Muthukannan, K. et al.(2015) ‘Classification of diseased plant leaves using neural network algorithms’, ARPN Journal of Engineering and Applied Sciences.
  • Picon, A. et al.(2018) ‘Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild’, Computers and Electronics in Agriculture. doi: 10.1016/j.compag.2018.04.002.
  • Ramya, V. and Lydia, M. A. (2016) ‘Leaf Disease Detection and Classification using Neural Networks’, International Journal of Advanced Research in Computer and Communication Engineering, 5(11), pp. 207–210. doi: 10.17148/IJARCCE.2016.51144.
  • Russell, S. andNorvig, P. (2002) Artificial Intelligence: A Modern Approach (2nd Edition), Prentice Hall. doi: 10.1017/S0269888900007724.
  • Sladojevic, S. et al.(2016) ‘Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification’, Computational Intelligence and Neuroscience, 2016. doi: 10.1155/2016/3289801.
  • Vapnik, V. N. (1999) ‘An overview of statistical learning theory’, IEEE Transactions on Neural Networks. doi: 10.1109/72.788640.
  • Wang, G., Sun, Y. and Wang, J. (2017) ‘Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning’, Computational Intelligence and Neuroscience. doi: 10.1155/2017/2917536.
  • Wang, H. et al.(2012) ‘Application of neural networks to image recognition of plant diseases’, in 2012 International Conference on Systems and Informatics, ICSAI 2012. doi: 10.1109/ICSAI.2012.6223479.

Combined UGV and UAV Perception of Field Areas as Operational Environments
Vasileios Moysiadis, Dimitrios Katikaridis, Giorgos Vasileiadis, Damianos Kalaitzidis, Panagiotis Papazisis, Aristotelis C. Tagarakis and Dionysis Bochtis
Pages: 41-46

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ABSTRACT: Detailed information of the structure of the surface in the extremely demanding and continuously changing environment ofagricultural fields is essential for the automated navigation of unmanned ground vehicles (UGV).Unmanned aerial vehicles (UAVs) can rapidly provide essential information for this purpose. Hence, in this work the conceptualization of an inter-communication system UAV and UGV communication is proposed. The aim of the developed system is the cooperative UAV -UGV path mapping procedure for large-scale areas in tandem with the depletion of the operational costs related to the operational environment awareness. In order to accomplish the above-mentioned concept, state of the art technologies and algorithms were incorporated. According to the concept, theUAV executes an automated flight, detects the cultivated trees, extracts their coordinates and sends them tothe UGV. As a final step, the UGV creates a pseudo 2D map with all the identified treesand the pathto follow during the operations in the field.The developed system was tested in real field conditions in a commercial walnut orchard providing satisfactory operation.

References

  • Amorós López, J., Izquierdo Verdiguier, E., Gómez Chova, L., Muñoz Marí, J., Rodríguez Barreiro, J. Z., Camps Valls, G. and Calpe Maravilla, J. (2011) ‘Land cover classification of VHR airborne images for citrus grove identification’, ISPRS Journal of Photogrammetry and Remote Sensing. Elsevier, 66(1), pp. 115–123. doi: 10.1016/J.ISPRSJPRS.2010.09.008.
  • Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G. and Bochtis, D. (2019) ‘Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review’, Remote Sensing. Multidisciplinary Digital Publishing Institute, 11(6), p. 676. doi: 10.3390/rs11060676.
  • Bochtis, D. D., Sørensen, C. G., Jørgensen, R. N. and Green, O. (2009) ‘Modelling of material handling operations using controlled traffic’, Biosystems Engineering, 103(4). doi: 10.1016/j.biosystemseng.2009.02.006.
  • Bochtis, D., Griepentrog, H. W., Vougioukas, S., Busato, P., Berruto, R. and Zhou, K. (2015) ‘Route planning for orchard operations’, Computers and Electronics in Agriculture, 113. doi: 10.1016/j.compag.2014.12.024.
  • Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (2017) Classification And Regression Trees. Routledge. doi: 10.1201/9781315139470.
  • Duda, R. O., Hart, P. E. (Peter E. and Stork, D. G. (2001) Pattern classification. Wiley.
  • Greenberg, J. A., Dobrowski, S. Z. and Ustin, S. L. (2005) ‘Shadow allometry: Estimating tree structural parameters using hyperspatial image analysis’, Remote Sensing of Environment. Elsevier, 97(1), pp. 15–25. doi: 10.1016/J.RSE.2005.02.015.
  • Hameed, I. A., Bochtis, D. D., Sørensen, C. G. and Vougioukas, S. (2012) ‘An object-oriented model for simulating agricultural in-field machinery activities’, Computers and Electronics in Agriculture, 81, pp. 24–32. doi: 10.1016/j.compag.2011.11.003
  • Hansen, K. D., Garcia-Ruiz, F., Kazmi, W., Bisgaard, M., la Cour-Harbo, A., Rasmussen, J. and Andersen, H. J. (2013) ‘An Autonomous Robotic System for Mapping Weeds in Fields’, IFAC Proceedings Volumes. Elsevier, 46(10), pp. 217–224. doi: 10.3182/20130626-3-AU-2035.00055.
  • Kurashiki, K., Fukao, T., Ishiyama, K., Kamiya, T. and Murakami, N. (2010) ‘Orchard traveling UGV using particle filter based localization and inverse optimal control’, in 2010 IEEE/SICE International Symposium on System Integration. IEEE, pp. 31–36. doi: 10.1109/SII.2010.5708297.
  • Liakos, K., Busato, P., Moshou, D., Pearson, S., Bochtis, D., Liakos, K. G., Busato, P., Moshou, D., Pearson, S. and Bochtis, D. (2018) ‘Machine Learning in Agriculture: A Review’, Sensors. Multidisciplinary Digital Publishing Institute, 18(8), p. 2674. doi: 10.3390/s18082674.
  • Ozdogan, B., Gacar, A. and Aktas, H. (2017) ‘DIGITAL AGRICULTURE PRACTICES IN THE CONTEXT OF AGRICULTURE 4.0’, Journal of Economics, Finance and Accounting-JEFA, 4(2), pp. 184–191. doi: 10.17261/Pressacademia.2017.448.
  • Tokekar, P., Hook, J. Vander, Mulla, D. and Isler, V. (2016) ‘Sensor Planning for a Symbiotic UAV and UGV System for Precision Agriculture’, IEEE Transactions on Robotics, 32(6), pp. 1498–1511. doi: 10.1109/TRO.2016.2603528.
  • Weltzien, C. (2016) ‘Digital agriculture-or why agriculture 4.0 still offers only modest returns’. doi: 10.15150/lt.2015.3123.
  • Yang, L., Wu, X., Praun, E. and Ma, X. (2009) ‘Tree detection from aerial imagery’, in Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, pp. 131–137.

Spatial Optimization for Orchards in Complex Field Areas
Giorgos Vasileiadis, Vasileios Moysiadis, Ioannis Menexes, Dimitrios Kateris and Dionysis Bochtis
Pages: 47-52

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ABSTRACT: The establishment is a cornerstone work for orchards and paradoxically is mainly based on empirical and traditional knowledge. This results to low or no improvement at all over the years and even less likelihood to adopt and harness the power of the new technological breakthroughs. The proposed system takes advantage of the abundant computational power to recognize and adapt planting patterns to complex field shapes. Enhancements include among others the integration of new spatial work requirements stemming from the emergent agri-robotics machinery field that can be input data for the design process. Also, the ability to dry run different planting patterns to fully optimize surface coverage. Furthermore, the work includes modules that quantify and integrate micro-climatic factors, optimizing in a non-uniform method the planting pattern. This feature is ground-breaking especially in cultivations that require pollinators, where standard practice was to set a percentile of pollinators, severely affecting productivity. In the work presented the algorithm is shown to be able to reduce the number of pollinators without decreasing their effectiveness, using grid deformation techniques, clustering algorithms with modified criteria imposed by the needs of the agronomic system. The results show significantly increased productivity potential attributed both to the reduced number of pollinators required and the increased spatial efficacy. Additionally, the fully digitized operation offers enhanced postprocessing capabilities to the farm manager as well as a digitized ground truthing tool.

References

  • Bell, G. and Fletcher, A. (1978) ‘Computer organised orchard layouts (COOL) based on the permutated neighbourhood design concept.’, Silvae Genetica, 27.
  • Bochtis, D. D., Sørensen, C. G., Busato, P., Hameed, I. A., Rodias, E., Green, O. and Papadakis, G. (2010) ‘Tramline establishment in controlled traffic farming based on operationalmachinery cost’, Biosystems Engineering, 107(3), pp. 221–231. doi: 10.1016/j.biosystemseng.2010.08.004.
  • Bochtis, D. D., Sørensen, C. G., Jørgensen, R. N. and Green, O. (2009) ‘Modelling of material handling operations using controlled traffic’, BiosystemsEngineering, 103(4). doi: 10.1016/j.biosystemseng.2009.02.006.
  • Bochtis, D., Griepentrog, H. W., Vougioukas, S., Busato, P., Berruto, R. and Zhou, K. (2015) ‘Route planning for orchard operations’, Computers and Electronics in Agriculture. Elsevier, 113, pp. 51–60. doi: 10.1016/j.compag.2014.12.024
  • Bochtis, D., Vougioukas, S., Ampatzidis, Y. and Tsatsarelis, C. (2007) ‘Field Operations Planning for Agricultural Vehicles: A Hierarchical Modeling Framework’, Agricultural Engineering International: the CIGR Journal of Scientific Research and Development. IX: Manuscript PM, IX, p. 21.
  • Chaloupková, K., Stejskal, J., El-Kassaby, Y. A. and Lstibůrek, M. (2016) ‘Optimum neighborhood seed orchard design’, Tree Genetics and Genomes. Springer Berlin Heidelberg, 12(6),p. 105. doi: 10.1007/s11295-016-1067-y.
  • Fountas, S., Søren Pedersen, M. and Blackmore, S. (2005) ‘ICT in Precision Agriculture–diffusion of technology’, ICT in agriculture: ..., pp. 1–15.
  • Giertych, M. (1975) ‘Seed orchard designs’, Seed orchards (Faulkner R, ed). Forestry Commission, Bulletin, (54), pp. 25–37.
  • Hameed, I. A., Bochtis, D. D., Sørensen, C. G., Jensen, A. L. and Larsen, R. (2013) ‘Optimized driving direction based on a three-dimensional field representation’, Computers and Electronics in Agriculture, 91. doi: 10.1016/j.compag.2012.12.009.
  • Hameed, I. A., Bochtis, D. D., Sørensen, C. G. and Nøremark, M. (2010) ‘Automated generation of guidance lines for operational field planning’, Biosystems Engineering, 107(4), pp. 294–306. doi: 10.1016/j.biosystemseng.2010.09.001.
  • Hameed, I. A., Bochtis, D. D., Sørensen, C. G. and Vougioukas, S. (2012) ‘An object-oriented model for simulating agricultural in-field machinery activities’, Computers and Electronics in Agriculture, 81, pp. 24–32. doi: 10.1016/j.compag.2011.11.003.
  • Sáez, A., di Virgilio, A., Tiribelli, F. and Geslin, B. (2018) ‘Simulation models to predict pollination success in apple orchards: a useful tool to test management practices’, Apidologie. Springer Paris, 49(5), pp. 551–561. doi: 10.1007/s13592-018-0582-2.
  • Won Suk Lee, Chinchuluun, R. and Ehsani, R. (2009) ‘CITRUS FRUIT IDENTIFICATION USING MACHINE VISION FOR A CANOPY SHAKE AND CATCH HARVESTER’, Acta Horticulturae, (824), pp. 217–222. doi: 10.17660/ActaHortic.2009.824.24.

Sustainability

Data Transmission and Management for Wireless Sensor Networks in German Dairy Farming Environments
Maximilian Treiber, Martin Höhendinger, Natascha Schlereth, Harald Rupp, Josef Bauerdick, Omar Hijazi and Heinz Bernhardt
Pages: 53-58

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ABSTRACT: The intensity of animal husbandry rises, as well as the regulation and documentation requirements every farmer must fulfil. Wireless Sensor Networks (WSN) are an option to improve efficiency and animal welfare on dairy farms. They provide information, that helps farmers to focus on the necessary chores and help automate documentation and control processes. However, a dairy farm is a difficult environment for wireless data transmission. This research shows the most common connectivity options in Germany and discusses the technologies regarding their usefulness for data transmission on a dairy farm. It is shown, that the digital transformation of a dairy farm requires different network technologies for different tasks. Data Management must integrate seamlessly and provide decision support as well as control over the farmer’s data. A powerful middleware based on broker models can be a solution to bring together live sensor data and information from existing information systems. The digital twin model of the TUM dairy research station is shown as example for the visualization of a custom user interface (UI). Intuitive UIs are needed for a successful adoption of the technology by farmers.

References

  • Broy, M.(2010) ‘Cyber-Physical Systems, Innovation Durch Software-Intensive Eingebettete Systeme’, Berlin, Heidelberg, Springer-Verlag, online available at http://dx.doi.org/10.1007/978-3-642-14901-6.
  • ioBroker GmbH (2019) ‘ioBroker, automate your life!’, Version 3.6.0, Karlsruhe, online available at http://www.iobroker.net, last checked 4/29/2019
  • Jungbluth, T.; Büscher, W.; Krause, M.(2017) ‘Technik Tierhaltung‘, 2.edition, Stuttgart, Verlag Eugen Ulmer (UTB, 2641).
  • KTBL (2007) ‘Precision Dairy Farming, Elektronikeinsatz in der Milchviehhaltung; KTBL-Tagung 2.-3.05.2007,Leipzig’, Darmstadt, KTBL Kuratorium für Technik und Bauwesen in der Landwirtschaft (KTBL-Schrift, 457)’, online available at http://deposit.d-nb.de/cgi-bin/dokserv?id=2948986&prov=M&dok_var=1&dok_ext=htm.
  • Mekki, Kais; Bajic, Eddy; Chaxel, Frederic; Meyer, Fernand (2018) ‘Overview of Cellular LPWAN Technologies for IoT Deployment, Sigfox, LoRaWAN, and NB-IoT. In: ‘2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). Athens, 19.03.2018 -23.03.2018’, IEEE, S. 197–202.
  • Miki, T.; Ohya, T.; Yoshino, H.; Umeda, N. (2005) ‘The Overview of the 4thGeneration Mobile Communication System. In: ‘2005 5th International Conference on Information Communications & Signal Processing’. Bangkok, Thailand, 06-09 Dec. 2005, IEEE, S. 1600–1604.
  • Raza, Usman; Kulkarni, Parag; Sooriyabandara, Mahesh (2017) ‘Low Power Wide Area Networks, An Overview. In: IEEE Commun. Surv. Tutorials 19 (2), S. 855–873. DOI: 10.1109/COMST.2017.2652320.
  • Ried, S. (2018) ‘IoT Connectivity -Der drahtlose Wegin die Cloud’, online available at https://www.crisp-research.com/iot-connectivity-der-drahtlose-weg-die-cloud/, last checked 2/22/2019.
  • Schön, H.(1993) ‘Elektronik und Computer in der Landwirtschaft, Rechnergestützte Verfahren für eine betriebsmittelsparende und umweltverträgliche Produktion’, Stuttgart, Ulmer.
  • Stumpenhausen, J.; Bernhardt, H.; Höld, M.; Gräff, A. (2018) ‘"Stall 4.0" -Forschungen für ein Integrated Dairy Farming. In: Tierärztliche Umschau 73 (10), S. 366–367.

Sediment Wattle Configuration and Optimization of Passive Polymer Application for Turbidity Reduction in Channelized Runoff
Calvin Sawyer, Charles Privette, James Berry and William Bridges
Pages: 59-64

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ABSTRACT: Effects of accelerated erosion resulting from such anthropogenic land disturbing activities as agriculture, timber,harvesting and construction are numerous and well-documented.In addition, elevated turbidity from eroded soilhas gained recognition as an indicator of sediment associated impairmentto water quality.Previous research has shown thatpassive polyacrylamide (PAM) can be effective in reducing soil erosion when appliedto irrigation water in agricultural settings. Water soluble anionic PAM was identified as highly effective at preventing erosion and increasing infiltration when used with furrow irrigation.The focus of this research was tomaximizeturbidity reduction within channelized flow using passive polyacrylamide (PAM) applications in associationwith excelsior fiber sediment wattle installation. Four treatments were appliedto assessvarious PAM applicationmethodsincluding; (i) a control with no PAM; (ii) granular PAM appliedin 100-g doses directly on each of five sediment wattles before five simulated runoff events; (iii) granular PAM appliedin 100-g doses directly on each of five sediment wattles only once before five simulated runoff events; (iv) granular PAM held in a permeable bag applied with 500-g doses. Results provide evidence that PAM application can be an effective practice for turbidity reduction within channels. Sediment wattles without PAM application provided no reduction in turbidity (F-stat = 0.0588, p = 0.9975, n = 60).Passively applied PAM was greatly more effective in reducing turbidity than the evaluated permeable PAM bag. Mean turbidity, over five simulated runoff events, was 202 NTU using three sediment wattles when PAM was applied. Thisresearch provides considerable evidence that highly turbid, sediment-laden channelized site runoff can be remediated using passive granular PAM application and sediment wattle installation.

References

  • American Excelsior Company. 2012. Curlex® Sediment Logs®. Retrieved from website: http://www.americanexcelsior.com/erosioncontrol/products/sedimentlogs.php
  • Line, D.E., and N.M. White. 2001. Efficiencies of Temporary Sediment Traps on Two North Carolina Construction Sites. Transactions of American Society of Agricultural Engineers 44(5):1207-1215.
  • McLaughlin, R.A. and McCaleb, M.M. (2010) Passive Treatment to Meet the EPA Turbidity Limit. American Society of Agricultural and Biological Engineering Presentation Paper Number: 711P0710cd, St. Joseph, MI.
  • McVan Instruments. 2012. Analite NEP160 Turbidity Meter for Field and Laboratory Applications. Retrieved from website: http://www.mcvan.com/images/stories/acrobat/nep160.pdf.
  • South Carolina Department of Transportation (SCDOT). 2011.Supplemental Technical Specification for Rolled Erosion Control Products (RECP). Retrieved from website: http://www.scdot.org/doing/technicalPDFs/supTechSpecs/SC-M-815-9.pdf.
  • Wu, J.S., R.E. Holman, andJ.R. Dorney. 1996. Systematic Evaluation of Pollutant Removal by Urban Detention Ponds. Journal of Environmental Engineering 122(1):983-988.

Assessing Agricultural Sustainability Within a Farm Management Information System: A Review of Indicators
Maria G. Lampridi and Dionysis Bochtis
Pages: 65-70

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ABSTRACT: The use of Farm Management Information Systems (FMIS) is spreading over the last years facilitating operational management leading to increased productivity while minimizing the relevant production costs. FMISs use indicators in order to benchmark the performance of a cultivation usually in terms of its economic return and its environmental impact. However, these are mostly standalone indicators that are not combined and holistically examined towards the determination of an agricultural system’s overall sustainability. It is also very important to note that the assessment of agricultural sustainability has been a continuous debate within the scientific community and still a commonly used methodology has not been established. Several methodologies and frameworks have been employed most of which use sets of indicators to assess the economic, environmental and social impacts of agricultural operations. Attempting to address the issue of sustainability benchmarking within a FMIS this paper presents a literature review of sustainability indicators that are used in agricultural sustainability studies at farm level. A total of 36 studies were thoroughly examined in order to extract the individual economic, environmental and social indicators that were employed. The indicators were categorized depending on the examined theme and a frequency analysis was conducted in order to determine the most frequently used. Ultimate goal of the review is to arrive at an easily computable and comprehensible system of indicators that could be used in a Farm Management Information System providing the stakeholders with integrated information regarding the overall sustainability performance of their cultivations.

References

  • Allahyari, M. S., Daghighi Masouleh, Z. and Koundinya, V. (2016) ‘Implementing Minkowski fuzzy screening, entropy, and aggregation methods for selecting agricultural sustainability indicators’, Agroecology and Sustainable Food Systems. Taylor & Francis, 40(3), pp. 277–294. doi: 10.1080/21683565.2015.1133467.
  • Van Asselt, E. D., Van Bussel, L. G. J., Van Der Voet, H., Van Der Heijden, G. W. A. M., Tromp, S. O., Rijgersberg, H., Van Evert, F., Van Wagenberg, C.P. A. and Van Der Fels-Klerx, H. J. (2014) ‘A protocol for evaluating the sustainability of agri-food production systems -A case study on potato production in peri-urban agriculture in the Netherlands’, Ecological Indicators. Elsevier Ltd, 43, pp. 315–321. doi: 10.1016/j.ecolind.2014.02.027.
  • Banias, G., Lampridi, M., Pediaditi, K., Achillas, C., Sartzetakis, E., Bochtis, D., Berruto, R. and Busato, P. (2017) ‘Evaluation of environmental impact assessment framework effectiveness’, Chemical Engineering Transactions, 58, pp. 805–810. doi: 10.3303/CET1758135.
  • Bockstaller, C., Guichard, L., Keichinger, O., Girardin, P., Galan, M. B. and Gaillard, G. (2009) ‘Review article Comparison of methods to assess the sustainability of agricultural systems . A review’, Agronomy, 29, pp. 223–235. doi: 10.1051/agro.
  • Cerutti, A. K., Bruun, S., Beccaro, G. L. and Bounous, G. (2011) ‘A review of studies applying environmental impact assessment methods on fruit production systems’, Journal of Environmental Management. Elsevier Ltd, 92(10), pp. 2277–2286. doi: 10.1016/j.jenvman.2011.04.018.
  • Gaviglio, A., Bertocchi, M. and Demartini, E. (2017) ‘A Tool for the Sustainability Assessment of Farms: Selection, Adaptation and Use of Indicators for an Italian Case Study’, Resources. doi: 10.3390/resources6040060.
  • Gómez-Limón, J. A. and Sanchez-Fernandez, G. (2010) ‘Empirical evaluation of agricultural sustainability using composite indicators’, Ecological Economics. Elsevier, 69(5), pp. 1062–1075. doi: 10.1016/j.ecolecon.2009.11.027.
  • Lampridi, M. G., Kateris, D., Vasileiadis, G., Marinoudi, V., Pearson, S., Sørensen, C. G., Balafoutis, A. and Bochtis, D. (2019) ‘A Case-Based Economic Assessment of Robotics Employment in Precision Arable Farming’, Agronomy, 9(4), p. 175. doi: 10.3390/agronomy9040175.
  • Lampridi, M. G., Sørensen, C. G. and Bochtis, D. (2019) ‘Agricultural Sustainability: A Review of Concepts and Methods’, Sustainability, 11(18), p. 5120. doi: 10.3390/su11185120
  • De Luca, A. I., Falcone, G., Iofrida, N., Stillitano, T., A., S. and Gulisano, G. (2015) ‘Life cycle methodologies to improve agri-food systems sustainability’, Rivista di Studi sulla Sostenibilita, 1, pp. 135–150.
  • Marinoudi, V., Sørensen, C. G., Pearson, S. and Bochtis, D. (2019) ‘Robotics and labour in agriculture. A context consideration’, Biosystems Engineering. Academic Press, 184, pp. 111–121. doi: 10.1016/J.BIOSYSTEMSENG.2019.06.013.
  • De Olde, E., Oudshoorn, F., Bokkers, E., Stubsgaard, A., Sørensen, C. and de Boer, I. (2016) ‘Assessing the Sustainability Performance of Organic Farms in Denmark’, Sustainability, 8(9), p. 957. doi: 10.3390/su8090957.
  • De Olde, E. M., Oudshoorn, F. W., Sørensen, C. A. G., Bokkers, E. A. M. and De Boer, I. J. M. (2016) ‘Assessing sustainability at farm-level: Lessons learned from a comparison of tools in practice’, Ecological Indicators. Elsevier, 66, pp. 391–404. doi: 10.1016/j.ecolind.2016.01.047.
  • Van Passel, S., Van Huylenbroeck, G., Lauwers, L. and Mathijs, E. (2009) ‘Sustainable value assessment of farms using frontier efficiency benchmarks’, Journal of Environmental Management. Elsevier Ltd, 90(10), pp. 3057–3069. doi: 10.1016/j.jenvman.2009.04.009.
  • Peano, C., Migliorini, P. andSottile, F. (2014) ‘A methodology for the sustainability assessment of agri-food systems: An application to the slow food presidia project’, Ecology and Society. doi: 10.5751/ES-06972-190424.
  • Pham, L. Van and Smith, C. (2014) ‘Drivers of agricultural sustainability in developing countries: A review’, Environment Systems and Decisions. Springer US, pp. 326–341. doi: 10.1007/s10669-014-9494-5.
  • Rodias, E., Berruto, R., Bochtis, D., Busato, P. and Sopegno, A. (2017) ‘A computational tool for comparative energycost analysis of multiple-crop production systems’, Energies, 10(7). doi: 10.3390/en10070831.
  • Rodias, E., Berruto, R., Busato, P., Bochtis, D., Sørensen, C. and Zhou, K. (2017) ‘Energy Savings from Optimised In-Field Route Planning for Agricultural Machinery’, Sustainability. Multidisciplinary Digital Publishing Institute, 9(11), p. 1956. doi: 10.3390/su9111956.
  • Rodias, E. C., Lampridi, M., Sopegno, A., Berruto, R., Banias, G., Bochtis, D. D. and Busato, P. (2019) ‘Optimal energy performance on allocating energy crops’, Biosystems Engineering. doi: 10.1016/j.biosystemseng.2019.02.007.
  • Sajjad, H. and Nasreen, I. (2016) ‘Assessing farm-level agricultural sustainability using site-specific indicators and sustainable livelihood security index: Evidence from Vaishali district, India’, Community Development. Routledge, 47(5), pp. 602–619. doi: 10.1080/15575330.2016.1221437.
  • Santiago-Brown, I., Metcalfe, A., Jerram, C. and Collins, C. (2015) ‘Sustainability assessment in wine-grape growing in the New World: Economic, environmental, and social indicators for agricultural businesses’, Sustainability (Switzerland), 7(7), pp. 8178–8204. doi: 10.3390/su7078178.
  • Snapp, S. S., Grabowski, P., Chikowo, R., Smith, A., Anders, E., Sirrine, D., Chimonyo, V. and Bekunda, M. (2018) ‘Maize yield and profitability tradeoffs with social, human and environmental performance: Is sustainable intensification feasible?’, Agricultural Systems, 162(April 2017), pp. 77–88. doi: 10.1016/j.agsy.2018.01.012

Ergonomics

Requirements for Automatic Feeding Systems in Southern German Dairy Farms
Fredrik Regler, Matthias Reger, Rosemarie Oberschätzl-Kopp, Jörn Stumpenhausen and Heinz Bernhardt
Pages: 71-76

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ABSTRACT: In recent years, automatic milking systems have found widespread use in southern Germany. For automatic feeding systems a similar trend is predicted. In an online survey, the technological and structural needs of farmers wereanalysed. It is revealed that the current situation and the desired situation on farms about feeding times differ from each other, which is a driving force. Most farmers feed twice a day(43%), fewer once a day(29%). 34 % of the respondents considerfeedingfour times a day and 31 % feedingthree timesa day as meaningful.It turns out that especially the technical reliabilityand safety for humans and animals are the “absolute priority” or “very important” for 92% bzw. 75%. Not a single respondent choose “undetermined” or “unimportant”. The first economic related priority is time savings. It shows that most people are not very familiar with automatic machinery and need to build up trust first, in advance to the known economic benefits.

References

  • Bernhardt, H. (2019) ‘Technik in der Rinderhaltung‘. In: Frerichs, Ludger (Hrsg.): Jahrbuch Agrartechnik 2018. Braunschweig: Institut für mobile Maschinen undNutzfahrzeuge, pp. 1-13. doi:10.24355/dbbs.084-201901211151-0
  • Douphrate, D., Nonnenmann M. and Rosecrance J. (2009)‘Ergonomics in Industrialized Dairy Operations’, Journal of Agromedicine, 14, pp.406-412, doi:10.1080/10599240903260444
  • Eichhorn, H. (1985) ‘Landtechnik‘,Verlag Eugen Ulmer, Stuttgart
  • Kolstrup, C., Kallioniemi, M., Lundqvist, P., Kymäläinen, H., Stallones L. and Brumby, S. (2013)‘International Perspectives on Psychosocial Working Conditions, Mental Health, and Stress of Dairy Farm Operators’, Journal of Agromedicine, 18, pp. 244-255, doi:10.1080/1059924X.2013.796903
  • Oberschätzl-Kopp, R. andHaidn, B. (2015) ‘DLG-Merkblatt 398-Automatische Fütterungssysteme für Rinder-Technik, Leistung, Planungshinweise‘, DLG Ausschuss für Technik in der tierischen Produktion.
  • Oberschätzl-Kopp, R., Haidn, B., Peis, R., Reiter,R. and Bernhardt, H. (2016)‘Untersuchungen zum Verhalten von Milchkühen bei automatischer Fütterungin einem AMS-Betrieb’, Landtechnik, 71, pp. 55-65. doi:10.15150/lt.2016.3122
  • Oberschätzl-Kopp, R., Bühler, J., Gräff, A., Wörz, S. and Bernhardt, H. (2018) ‘Studies on electrical energy consumption of an automatic feeding system in dairy cattle farming’, 2018 ASABE Annual International Meeting 1800560. doi:10.13031/aim.201800560
  • Ordolff, D. (2001) ‘Introduction of electronics into milking technology’, Computers andElectronics in Agriculture, 30, pp.125-149.doi:10.1016/S0168-1699(00)00161-7

Large-Scale Point-Cloud Based Global Mapping for Orchard Operations
Dimitrios Karikaridis, Vasileios Moysiadis, Dimitrios Kateris and Dionysis Bochtis
Pages: 77-82

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ABSTRACT: Robotic motion in orchard fields consists of several components such as mapping, perception, navigation, and route (path-motion) planning. Route planning and navigation are highly contingent on mapping functionality whilst the robotic vehicle operates and adapts itself into a partially known work area providing a safe and accurate routing. Traditional mapping techniques entail an unmanned ground vehicle equipped with laser scan sensors and inertial measurement units resulting to a spatial 3-dimension map, which is a comprehensive guide for the robotic vehicle. The proposed system here takes advantage of the complementary mapping operation of an unmanned aerial vehicle’s ample flight height for enhancing its mapping ability. This approach can provide a ground-breaking perception solution especially in agricultural fields, where the targeted area covers extremely wide-open spaces. This combined mapping process reduces the time needed by a ground vehicle for mapping the environment by itself, while it reduces the risk of accidents and operational failures. Furthermore, the ability to implement a camera and a GPS sensor on the vehicle, enables the tree indexing resulting to a significantly more accurate ground vehicle navigation. Additionally, the trees are associated with their geolocation providing future applications with valuable information. The digital map documentation is compliant with a seamless integration with the precision agriculture framework, enhancing field mapping value.

References

  • Bochtis, D., Vougioukas, S., Ampatzidis, Y. and Tsatsarelis, C. (2007) ‘Field Operations Planning for Agricultural Vehicles: A Hierarchical Modeling Framework’, Agricultural Engineering International: the CIGR Journal of Scientific Research and Development. IX: Manuscript PM, IX, p. 21
  • Bosse, M. and Zlot, R. (2008) ‘Map Matching and Data Association for Large-Scale Two-dimensional Laser Scan-based SLAM’, The International Journal of Robotics Research, 27(6), pp. 667–691. doi: 10.1177/0278364908091366.
  • Bosse, M. and Zlot, R. (2009) ‘Continuous 3D scan-matching with a spinning 2D laser’, in 2009 IEEE International Conference on Robotics and Automation. IEEE, pp. 4312–4319. doi: 10.1109/ROBOT.2009.5152851.
  • Cole, D. M. and Newman, P. M. (2006) ‘Using laser range data for 3D SLAM in outdoor environments’, in Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006. IEEE, pp. 1556–1563. doi: 10.1109/ROBOT.2006.1641929.
  • Comba, L., Biglia, A., Ricauda Aimonino, D. and Gay, P. (2018) ‘Unsupervised detection of vineyards by 3D point-cloud UAV photogrammetry for precision agriculture’, Computers and Electronics in Agriculture. Elsevier, 155, pp. 84–95. doi: 10.1016/J.COMPAG.2018.10.005.
  • Hameed, I. A., Bochtis, D. D., Sørensen, C. G., Jensen, A. L. and Larsen, R. (2013) ‘Optimized driving direction based on a three-dimensional field representation’, Computers and Electronics in Agriculture, 91. doi: 10.1016/j.compag.2012.12.009.
  • Hastaoğlu,K. Ö., Gül, Y., Poyraz, F. and Kara, B. C. (2019) ‘Monitoring 3D areal displacements by a new methodology and software using UAV photogrammetry’, International Journal of Applied Earth Observation and Geoinformation. Elsevier, 83, p. 101916. doi: 10.1016/J.JAG.2019.101916.
  • Hsu, Y.-W., Huang, S.-S. and Perng, J.-W. (2018) ‘Application of multisensor fusion to develop a personal location and 3D mapping system’, Optik. Urban & Fischer, 172, pp. 328–339. doi: 10.1016/J.IJLEO.2018.07.029.
  • McCormac, J., Handa, A., Davison, A. and Leutenegger, S. (2016) ‘SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks’.
  • Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R. and Mg, A. (2009) ‘ROS: an open-source Robot Operating System’, Icra, 3(Figure 1), p. 5. doi: http://www.willowgarage.com/papers/ros-open-source-robot-operating-system.
  • Salas-Moreno, R. F., Newcombe, R. A., Strasdat, H., Kelly, P. H. J. and Davison, A. J. (2013) ‘SLAM++: Simultaneous Localisation and Mapping at the Level of Objects’, in 2013 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp. 1352–1359. doi: 10.1109/CVPR.2013.178.
  • Sünderhauf, N., Pham, T. T., Latif, Y., Milford, M. and Reid, I. (2016) ‘Meaningful Maps With Object-Oriented Semantic Mapping’.
  • Torres-Sánchez, J., de Castro, A. I., Peña, J. M., Jiménez-Brenes, F. M., Arquero, O., Lovera, M. and López-Granados, F. (2018) ‘Mapping the 3D structure of almond trees using UAV acquired photogrammetric point clouds and object-based image analysis’, Biosystems Engineering. Academic Press, 176, pp. 172–184. doi: 10.1016/J.BIOSYSTEMSENG.2018.10.018.
  • Wu, Z., Ni, M., Hu, Z., Wang, J., Li, Q. and Wu, G. (2019) ‘Mapping invasive plant with UAV-derived 3D mesh model in mountain area—A case study in Shenzhen Coast, China’, International Journal of Applied Earth Observation and Geoinformation. Elsevier, 77, pp. 129–139. doi: 10.1016/J.JAG.2018.12.001.

Agri-Chains

Assessing Feasibility of Soybean Substitution With Alternative Leguminous Crops for South East European Countries
Spyros Niavis, Christina Kleisiari, Leonidas-Sotirios Kyrgiakos and George Vlontzos
Pages: 83-88

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ABSTRACT: It is a fact that there is a high trade deficit (around 70%) in Europe with regardsto protein crops. This is asignificant reason for the EU to implement policies aiming at reducing the dependence of imported materials with high protein content for animal feeduse. Due to their competitive prices, soybeans imports from the USAand China are particularly attractive choicesfor European importers and feed users. A very promising policy approach to tackle this phenomenon seems to be the implementation of production protocols for the legumes cultivation,based on the sustainability and use of economically and environmentally friendlypractices in accordance with European directives. Following this line of reasoning andtaking into account all the existing constraints in Southern Europefor achieving sustainability, this work explores a time period 2007-2016 evaluating four main high protein cropsso as to evaluate the feasibility ofa successful soybean substitution. Pisum sativum subsp. arvense L. and Lupinus albus are -the species primarily examined in this research, in an attempt to replace partially or completelythe soybean meal in the dairy cow diet. In addition, this research evaluates the impact of these legumes on the environment through crop rotation, which has significant implications for environmentalindicators improvement. Considering the actions presented in the second pillar of the EU Common Agricultural Policy, an attempt is made to replace the imported soybeans while preserving nationalnatural resources. EU policy should supportthe cultivation of such local high protein crops in order to achieve Sustainable Development Goals.

References

  • Baddeley, J. A., Pappa, V. A., Pristeri, A., Bergkvist, G., Monti, M., Reckling, M., ... Watson, C. A. (2017). Legume-based green manure crops. Legumes in Cropping Systems, (May 2018), 125–138. https://doi.org/10.1079/9781780644981.0125
  • European Commission. (2018). Report from the Commission to the Council and the European Parliament on the development of plant proteins in the European Union. Retrieved October 23, 2019, from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52018DC0757
  • EUROSTAT(2019) EUROSTATAvailable at: https://agridata.ec.europa.eu/extensions/DashboardCereals/OilseedProduction.html(Accessed: 24/2/2019)
  • FAOSTAT (2019) . Available at:http://www.fao.org/faostat/en/#data(Accessed: 27/2/2019)
  • FAO. (2009). Livestock production systems and ecosystems. The State of Food and Agriculture 2009, 53–74. Retrieved from http://www.fao.org/docrep/012/i0680e/i0680e04.pdf%0A0e04.pdf
  • Palhares, J. C. P., & Pezzopane, J. R. M. (2015). Water footprint accounting and scarcity indicators of conventional and organic dairy production systems. Journal of Cleaner Production, 93, 299–307. https://doi.org/10.1016/j.jclepro.2015.01.035
  • Stagnari, F., Maggio, A., Galieni, A., & Pisante, M. (2017). Multiple benefits of legumes for agriculture sustainability: an overview. Chemical and Biological Technologies in Agriculture, 4(1), 1–13. https://doi.org/10.1186/s40538-016-0085-1
  • Tufarelli, V., Khan, R. U., & Laudadio, V. (2012). Evaluating the suitability of field beans as a substitute for soybean meal in early-lactating dairy cow: Production and metabolic responses. Animal Science Journal, 83(2), 136–140. https://doi.org/10.1111/j.1740-0929.2011.00934.x
  • US Department of Agriculture, USDA Foreign Agricultural Service. (2019). Import volume of soybeans worldwide in 2018/19, by country (in million metric tons). Statista. Statista Inc.. Accessed: October 22, 2019. https://www.statista.com/statistics/612422/soybeans-import-volume-worldwide-by-country/

Cross-Cutting Themes

Investigating Ways to Develop and Control a Multi Purpose and Low Cost Agricultural Robotic Vehicle, in Scale
Dimitrios Loukatos, George Tzaninis, Konstantinos G. Arvanitis and Nikolaos Armonis
Pages: 89-94

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ABSTRACT: Agriculture is a sector that is rapidly changing due to the technological advances of our era, but is not always easy for the students to catch up with this fast evolving process. A good way to tackle it is to let them experiment trying to develop innovative roboticvehiclesfor agricultural purposes. This paper describes exactly the trials being made to design and implement anelectric robotic vehicle, inscale, in a cost-effective manner, using metal, wood,recyclable materials and small motors. Students experimented with bipolar stepper motors as well as with brushed DC motors and performed a comparative study between the two types. Starting from configurations involving only one ArduinoUno board,studentsshifted to scenarios with more components,and even with Raspberry Pi units,in order to better understand fundamental automatic control principlesand remote operation issues. The trials made to properly program the robot using both visual and textual programming environments are also reported. Most of the remote interaction scenarios have been carried out through Wi-Fi interfaces, while some of them involved LoRa interfaces to extend the effective controlling distance of the robot. For better efficiencyand autonomy, asmall solar panel unit has been adapted on the top of the robot and energy consumption for different configurations has been studiedas well.Finally, the paper, going beyond strictly educational purposes,reports oncharacteristic derived robotic layouts and proposes possible“real-world” use case scenarios.

References

  • ArduinoUno(2019). ArduinoUno board description on the official Arduino site. Retrieved inApril 2019from the site: https://store.arduino.cc/arduino-uno-rev3
  • Bechar, A. and Vigneault, C. (2016) ‘Agricultural robots for field operations: Concepts and components’, Biosystems Engineering, 149, pp. 94–111. doi: 10.1016/j.biosystemseng.2016.06.014
  • Bechar, A. and Vigneault, C. (2017) ‘Agricultural robots for field operations. Part 2: Operations and systems’, Biosystems Engineering, 153, pp. 110–128. doi: 10.1016/j.biosystemseng.2016. 11.004
  • Doran, M. V, and Clark, G. W. (2018)‘Enhancing Robotic Experiences throughout the Computing Curriculum’,SIGCSE’18, February 21-24, Baltimore, MD, USA (pp. 368–371).
  • FAO (2013) ‘Climate-smart agriculture sourcebook’, Food and Agriculture Organization of the United Nations.Retrievedin April 2019 from the site:http://www.fao.org/3/i3325e/i3325e.pdf. Accessed in April 2019.
  • Krishna, K.R., (2016), ‘Push button Agriculture: Robotics, drones, satellite-guided soil and crop management’, Apple Academic Press, Oakville, Ontario, Canada.ISBN-13: 978-1-77188-305-4 (eBook -PDF).
  • LoRa (2019). LoRa protocol description on Wikipedia. Retrieved in April 2019 from: https://en.wikipedia.org/wiki/LoRa
  • Loukatos, D., Kahn K. and Alimisis D., (2018) ‘Flexible Techniques for Fast Developing and Remotely Controlling DIY Robots, with AI flavor’, Proceedings of the ‘Educational Robotics 2018 (EDUROBOTICS)’, Rome, Italy, published by Springer, ISBN 978-3-030-18141-3
  • Raspberry (2019). Raspberry Pi 3 Model B board description on the official Raspberry site. Retrieved in Aprilof 2019from the site: https://www.raspberrypi.org/products/raspberry-pi-3-model-b/
  • Symeonaki, E.G., Arvanitis, K.G. and Piromalis, D.D. (2019), ‘Cloud computing for IoT applications in climate-smart agriculture: A review on the trends and challenges towards sustainability’. In Theodoridis, A., Ragkos, A. and Salampasis, M.(Eds.), Innovative Approaches and Applications for Sustainable Rural Development, HAICTA 2017, Springer, Cham, Earth System Sciences Series,Vol. 29, Chapter 9, pp. 147-167, 2019. DOI: 10.1007/978-3-030-02312-6_9.
  • UK-RAS Network: Robotics &Autonomous Systems (2018), ‘Agricultural robotics: The future of agricultural robots”, UK-RAS White Papers, ISSN: 2398-4414. Online at: https://arxiv.org/ ftp/arxiv/papers/1806/1806.06762.pdf.
  • Wi-Fi (2019). The IEEE 802.11 Standard. Retrieved in April 2019 from the site: http://www.ieee802.org/11/

Soil Organic Carbon Estimation With the Use of Proximal Visible Near Infrared Soil Spectroscopy
Theodora Angelopoulou, Athanasios Balafoutis, Georgios Zalidis and Dionysis Bochtis
Page: 95-100

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ABSTRACT: Soil is an important natural resource, thus monitoring soils’condition in an efficient and quantifiable way is considered of great importance for site specific management practices. However, soil properties estimation is a laborious procedure that entails great amount of cost and time. To address the need for soil information at large scales,proximal sensing applications are considered as an alternative to analytical wet chemistry. In particular, soil reflectance spectroscopy in the visible and near infrared region (400-2500nm) has been evaluatedwith promising results. The use of proximal sensing techniques for rapid in situ applications comprisesensors mounted on tractors or at a handheld mode. Soil organic carbon (SOC)is the most widelyinvestigated soil property due to its significance as itaffects most of the processes relatedto soil functions andhas presented good correlation with electromagnetic radiation.This workaims to provide a short review of proximal sensing techniques for SOC estimation.It was found that although results have been very promising there are still challengesto be addressed concerningfactorsthat affect measurementsi.e. soil moisture and soil roughness.

References

  • Bartholomeus, H., Kooistra, L., Stevens, A., van Leeuwen, M., van Wesemael, B., Ben-Dor, E., Tychon, B. (2011) ‘Soil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy’, International Journal of Applied Earth Observation and Geoinformation. Elsevier, 13(1), pp. 81–88. doi: 10.1016/J.JAG.2010.06.009.
  • Cambou, A., Cardinael, R., Kouakoua, E., Villeneuve, M., Durand, C., Barthès, B.G. (2016) ‘Prediction of soil organic carbon stock using visible and near infrared reflectance spectroscopy (VNIRS) in the field’, Geoderma. Elsevier, 261, pp. 151–159. doi: 10.1016/j.geoderma.2015.07.007.
  • Christy, C. D. (2007) ‘Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy’. doi: 10.1016/j.compag.2007.02.010.
  • England, J.R. and Viscarra Rossel, R.A. (2018) ‘Proximal sensing for soil carbon accounting’, 45194, pp. 101–122. doi: 10.5194/soil-4-101-2018.
  • FAO (2017) Soil Organic Carbon the Hidden Potential, Food and Agriculture Organization of the United Nations Rome,Italy. doi: 10.1038/nrg2350.
  • Franceschini, M.H.D., Demattê, J.A.M., Kooistra, L., Bartholomeus, H., Rizzo, R., Fongaro, C.T., Molin, J.P. (2018) ‘Effects of external factors on soil reflectance measured on-the-go and assessment of potential spectral correction through orthogonalisation and standardisation procedures’, Soil and Tillage Research. Elsevier, 177, pp. 19–36. doi: 10.1016/j.still.2017.10.004.
  • Ge, Y., Thomasson, J. A. and Sui, R. (2011) ‘Remote sensing of soil properties in precision agriculture: A review’, Frontiers of Earth Science, 5(3), pp. 229–238. doi: 10.1007/s11707-011-0175-0.
  • Gras, J.P., Barthès, B.G., Mahaut, B., Trupin, S. (2014) ‘Best practices for obtaining and processing field visible and near infrared (VNIR) spectra of topsoils’, Geoderma, 214–215. doi: 10.1016/j.geoderma.2013.09.021.
  • Jandl, R., Rodeghiero, M., Martinez, C., Cotrufo, M.F., Bampa, F., van Wesemael, B., Harrison, R.B., Guerrini, I.A., Richter, D. deB, Rustad, L. ‘Current status, uncertainty and future needs in soil organic carbon monitoring’, Science of The Total Environment. Elsevier, 468–469, pp. 376–383. doi: 10.1016/J.SCITOTENV.2013.08.026.
  • Knadel, M., Thomsen, A., Schelde, K., Greve, M.H. (2015) ‘Soil organic carbon and particle sizes mapping using vis–NIR, EC and temperature mobile sensor platform’, Computers and Electronics in Agriculture. Elsevier, 114, pp. 134–144. doi: 10.1016/J.COMPAG.2015.03.013.
  • Kodaira, M. and Shibusawa, S. (2013) ‘Using a mobile real-time soil visible-near infrared sensor for high resolution soil property mapping’, Geoderma. Elsevier, 199, pp. 64–79. doi: 10.1016/J.GEODERMA.2012.09.007.
  • Kuang, B. and Mouazen, A. M. (2013) ‘Non-biased prediction of soil organic carbon and total nitrogen with vis–NIR spectroscopy, as affected by soil moisture content and texture’, Biosystems Engineering. Academic Press, 114(3), pp. 249–258. doi: 10.1016/J.BIOSYSTEMSENG.2013.01.005.
  • Kühnel, A. and Bogner, C. (2017) ‘In-situ prediction of soil organic carbon by vis–NIR spectroscopy: an efficient use of limited field data’, European Journal of Soil Science, 68(5), pp. 689–702. doi: 10.1111/ejss.12448.
  • Kweon, G., Lund, E. and Maxton, C. (2013) ‘Soil organic matter and cation-exchange capacity sensing with on-the-go electrical conductivity and optical sensors’, Geoderma. Elsevier, 199, pp. 80–89. doi: 10.1016/j.geoderma.2012.11.001.
  • Mouazen, A. M. and Ramon, H. (2006) ‘Development of on-line measurement system of bulk density based on on-line measured draught, depth and soil moisture content’, Soil and Tillage Research, 86(2), pp. 218–229. doi: 10.1016/j.still.2005.02.026.
  • Nocita, M., Stevens, A., Noon, C., Van Wesemael, B. (2013) ‘Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy’, Geoderma, 199, pp. 37–42. doi: 10.1016/j.geoderma.2012.07.020.
  • Rodionov, A., Pätzold, S., Welp, G., Pallares, R.C., Damerow, L., Amelung, W. (2014) ‘Sensing of Soil Organic Carbon Using Visible and Near-Infrared Spectroscopy at Variable Moisture and Surface Roughness’, Soil Science Society of America Journal, 78(3), p. 949. doi: 10.2136/sssaj2013.07.0264.
  • Rodionov, A., Welp, G., Damerow, L., Berg, T., Amelung, W., Pätzold, S. (2015) ‘Towards on-the-go field assessment of soil organic carbon using Vis-NIR diffuse reflectance spectroscopy: Developing and testing a novel tractor-driven measuring chamber’, Soil and Tillage Research. Elsevier, 145, pp. 93–102. doi: 10.1016/j.still.2014.08.007.
  • Rodionov, A., Pätzold, S., Welp, G., Pude, R., Amelung, W. (2016) ‘Proximal field Vis-NIR spectroscopy of soil organic carbon: A solution to clear obstacles related to vegetation and straw cover’, Soil and Tillage Research, 163, pp. 89–98. doi: 10.1016/j.still.2016.05.008.
  • Sinfield, J. V., Fagerman,D. and Colic, O. (2010) ‘Evaluation of sensing technologies for on-the-go detection of macro-nutrients in cultivated soils’, Computers and Electronics in Agriculture. Elsevier, pp. 1–18. doi: 10.1016/j.compag.2009.09.017.
  • Viscarra Rossel, R.A., Lobsey, C.R., Sharman, C., Flick, P., McLachlan, G. (2017) ‘Novel Proximal Sensing for Monitoring Soil Organic C Stocks and Condition’, Environmental Science and Technology, 51(10), pp. 5630–5641. doi: 10.1021/acs.est.7b00889.
  • Wetterlind, J., Stenberg, B. and Rossel, R. A. V. (2013) ‘Soil analysis using visible and near infrared spectroscopy.’, Methods in molecular biology (Clifton, N.J.), 953, pp. 95–107. doi: 10.1007/978-1-62703-152-3_6