Implementation and applications of harvest fleet route planning
Nøgleord:
Internet of Things, Optimised Route Planning, Harvest Fleet Route Planning, Infield path planning, Harvest operations, Selective Harvesting, Soil Compaction ReductionSynopsis
In order to support the growing global population, it is necessary to increase food production efficiency and at the same time reduce its negative environmental impacts. This can be achieved by integrating diverse strategies from different scientific disciplines. As agriculture is becoming more data-driven by the use of technologies such as the Internet of Things, the efficiency in agricultural operations can be optimised in a sustainable manner. Some field operations, such as harvesting, are more complex and have higher potential for improvement than others, as they involve multiple and diverse vehicles with capacity constraints that require coordination. This can be achieved by optimised route planning, which is a combinatorial optimisation problem. Several studies have proposed different approaches to solve the problem. However, these studies have mainly a theoretical computer science perspective and lack the system perspective that covers the practical implementation and applications of optimised route planning in all field operations, being harvesting an important example to focus on. This requires an interdisciplinary approach, which is the aim of this Ph.D. project.
The research of this Ph.D. study examined how Internet of Things technologies are applied in arable farming in general, and in particular in optimised route planning. The technology perspective of the reviewing process provided the necessary knowledge to address the physical implementation of a harvest fleet route planning tool that aims to minimise the total harvest time. From the environmental point of view, the risk of soil compaction resulting from vehicle traffic during harvest operations was assessed by comparing recorded vehicle data with the optimised solution of the harvest fleet route planning system. The results showed a reduction in traffic, which demonstrates that these optimisation tools can be part of the soil compaction mitigation strategy of a farm. And from the economic perspective, the optimised route planner of an autonomous field robot was employed to evaluate the economic consequences of altering the route in selective harvesting. The results presented different scenarios where selective harvest was not economically profitable. The results also identified some cases where selective harvest has the potential to become profitable depending on grain price differences and operational costs. In conclusion, these different perspectives to harvest fleet route planning showed the necessity of assessing future implementation and potential applications through interdisciplinarity.
Referencer
Aasha Nandhini, S. et al. (2017) ‘Web Enabled Plant Disease Detection System for Agricultural Applications Using WMSN’, Wireless Personal Communications. Springer US, pp. 1–16. doi: 10.1007/s11277-017-5092-4.
Abdel-basset, M., Shawky, L. A. and Eldrandaly, K. (2018) ‘Grid quorum-based spatial coverage for IoT smart agriculture monitoring using enhanced multi-verse optimizer’, Neural Computing and Applications. Springer London, 4. doi: 10.1007/s00521-018-3807-4.
Abrahamsen, P. and Hansen, S. (2000) ‘Daisy : an open soil-crop-atmosphere system model’, Environmental Modelling & Software, 15, pp. 313–330. doi: https://doi.org/10.1016/S1364-8152(00)00003-7.
AEF (2020) New guideline for Extended Farm Management Information Systems Data Interface (EFDI) available. Available at: https://www.aef-online.org/news/news/news/new-guideline-for-extended-farm-management-information-systems-data-interface-efdi-available.html?tx_news_pi1%5Bcontroller%5D=News&tx_news_pi1%5Baction%5D=detail&cHash=c576abafcd100131a71123df3bb907fb.
AgroIntelli (2021) Versatile and autonomous agricultural robot solving multiple tasks in the field. Available at: https://www.agrointelli.com/robotti/ (Accessed: 14 April 2021).
Ahmed, E. M. E., Abdalla, K. H. B. and Eltahir, I. K. (2018) ‘Farm Automation based on IoT’, in 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE, pp. 1–4.
Ahmed, N. et al. (2018) ‘Internet of Things ( IoT ) for Smart Precision Agriculture and Farming in Rural Areas’, IEEE Internet of Things Journal. IEEE, 5(6), pp. 4890–4899. doi: 10.1109/JIOT.2018.2879579.
Alahmadi, A. et al. (2017) ‘Wireless Sensor Network With Always Best Connection for Internet of Farming’, in Mohanan, V., Budiarto, R., and Aldmour, I. (eds) Powering the Internet of Things With 5G Networks. Hershey, PA, USA: IGI Global, pp. 176–201. doi: 10.4018/978-1-5225-2799-2.ch007.
Alakukku, L. et al. (2003) ‘Prevention strategies for field traffic-induced subsoil compaction: A review Part 1. Machine/soil interactions’, Soil and Tillage Research, 73(1–2), pp. 145–160. doi: 10.1016/S0167-1987(03)00107-7.
Alblas, J. et al. (1994) ‘Impact of traffic-induced compaction of sandy soils on the yield of silage maize in The Netherlands’, Soil & Tillage Research, 29, pp. 157–165. doi: 10.1016/0167-1987(94)90052-3.
Aliev, K. (2018) ‘Internet of Plants Application for Smart Agriculture’, IJACSA - International Journal of Advanced Computer Science and Applications, 9(4), pp. 421–429. doi: 10.14569/IJACSA.2018.090458.
Araújo, S. O. et al. (2021) ‘Characterising the Agriculture 4 . 0 Landscape — Emerging Trends , Challenges and Opportunities’, Agronomy Journal, 11(667), pp. 1–37. doi: 10.3390/agronomy11040667.
Arvidsson, J. and Håkansson, I. (1991) ‘A model for estimating crop yield losses caused by soil compaction’, Soil & Tillage Research, 20(2–4), pp. 319–332. doi: 10.1016/0167-1987(91)90046-Z.
Ashton, K. (2009) ‘That “Internet of Things” Thing’, RFID Journal, June. Available at: http://www.rfidjournal.com/articles/view?4986.
Bacco, M., Berton, A., Gotta, A., et al. (2018) ‘IEEE 802.15.4 Air-Ground UAV Communications in Smart Farming Scenarios’, IEEE Communications Letters. IEEE, 22(9), pp. 1910–1913. doi: 10.1109/LCOMM.2018.2855211.
Bacco, M., Berton, A., Ferro, E., et al. (2018) ‘Smart Farming : Opportunities , Challenges and Technology Enablers’, in 2018 IoT Vertical and Topical Summit on Agriculture - Tuscany (IOT Tuscany). IEEE, pp. 1–6. doi: 10.1109/IOT-TUSCANY.2018.8373043.
Bakhtiari, A. et al. (2011) ‘Optimal route planning of agricultural field operations using ant colony optimization’, Agricultural Engineering International: CIGR Journal, 13(4), pp. 1–16. Available at: http://www.cigrjournal.org/index.php/Ejounral/article/view/1939.
Bakhtiari, A. et al. (2013) ‘Operations planning for agricultural harvesters using ant colony optimization’, Spanish
Journal of Agricultural Research, 11(3), pp. 652–660. doi: 10.5424/sjar/2013113-3865.
Bakker, D. M. and Davis, R. J. (1995) ‘Soil deformation observations in a vertisol under field traffic’, Australian Journal of Soil Research, 33(5), pp. 817–832. doi: 10.1071/SR9950817.
Balbuena, R. H. et al. (2000) ‘Soil compaction by forestry harvester operation. Evolution of physical properties’, Revista Brasileira de Engenharia Agrícola e Ambiental, 4(3), pp. 453–459.
Balducci, F. et al. (2018) ‘Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement’, Machines, 6(38), pp. 1–22. doi: 10.3390/machines6030038.
Balmos, A. D. et al. (2016) ‘Investigation of Bluetooth Communications for Low-Power Embedded Sensor Networks in Agriculture’, Transactions of the ASABE, 59(5), pp. 1021–1029. doi: 10.13031/trans.59.11173.
Barnes, A. P. et al. (2019) ‘Land Use Policy Exploring the adoption of precision agricultural technologies : A cross regional study of EU farmers’, Land Use Policy. Elsevier, 80(August 2018), pp. 163–174. doi: 10.1016/j.landusepol.2018.10.004.
Basnet, C. B., Foulds, N. R. and Wilson, J. R. (2006) ‘Scheduling Contractors’ Farm-to-Farm Crop Harvesting Operations’, International Transactions in Operational Research, 13(1), pp. 1–15. doi: 10.1111/j.1475-3995.2006.00530.x.
Basso, B. et al. (2009) ‘Landscape Position and Precipitation Effects on Spatial Variability of Wheat Yield and Grain Protein in Southern’, Journal of Agronomy and Crop Science, 195(4), pp. 301–312. doi: 10.1111/j.1439-037X.2008.00351.x.
Batey, T. (2009) ‘Soil compaction and soil management – a review’, Soil Use and Management, 25, pp. 335–345. doi: 10.1111/j.1475-2743.2009.00236.x.
Bauer, J. and Aschenbruck, N. (2018) ‘Design and Implementation of an Agricultural Monitoring System for Smart Farming’, in 2018 IoT Vertical and Topical Summit on Agriculture. Tuscany, Italy: IEEE, pp. 1–6. doi: 10.1109/IOT-TUSCANY.2018.8373022.
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.
Bechtsis, D. et al. (2017) ‘Sustainable supply chain management in the digitalisation era: The impact of Automated Guided Vehicles’, Journal of Cleaner Production, 142, pp. 3970–3984. doi: 10.1016/j.jclepro.2016.10.057.
Bengough, A. G. et al. (2011) ‘Root elongation water stress, and mechanical impedance: a review of limiting stresses and beneficial root tip traits’, J. Exp. Bot., 62, pp. 59–68.
Bennett, J. M. (2015) ‘Agricultural Big Data : Utilisation to Discover the Unknown and Instigate Practice Change’, Farm Policy Journal, 12(1), pp. 43–50. Available at: http://www.farminstitute.org.au/publications-1/farm-policy-journals/2015-autumn-from-little-data-big-data-grow/agricultural-big-data-utilisation-to-discover-the-unknown-and-instigate-practice-change.
Bermeo-Almeida, O. et al. (2018) ‘Blockchain in Agriculture : A Systematic Literature Review’, in 4th International Conference, CITI 2018, Proceedings. Guayaquil, Ecuador: Springer, pp. 44–56. doi: 10.1007/978-3-030-00940-3.
Bochtis, D. D. et al. (2013) ‘Benefits from optimal route planning based on B-patterns’, Biosystems Engineering. IAgrE, 115(4), pp. 389–395. doi: 10.1016/j.biosystemseng.2013.04.006.
Bochtis, D. D. and Sørensen, C. G. (2009) ‘The vehicle routing problem in field logistics part I’, Biosystems Engineering, 104(4), pp. 447–457. doi: 10.1016/j.biosystemseng.2009.09.003.
Bochtis, D. D. and Sørensen, C. G. (2010) ‘The vehicle routing problem in field logistics : Part II’, Biosystems Engineering, 105, pp. 180–188. doi: 10.1016/j.biosystemseng.2009.10.006.
Bochtis, D. D., Sørensen, C. G. C. and Busato, P. (2014) ‘Advances in agricultural machinery management : A review’, Biosystems Engineering. Elsevier Ltd, 126, pp. 69–81. doi: 10.1016/j.biosystemseng.2014.07.012.
Bochtis, D. D., Sørensen, C. G. and Green, O. (2012) ‘A DSS for planning of soil-sensitive field operations’, Decision Support Systems. Elsevier B.V., 53(1), pp. 66–75. doi: 10.1016/j.dss.2011.12.005.
Bochtis, D., Green, O. and Sørensen, C. G. (2011) ‘Spatio-temporal Constrained Planning Software for Field Machinery’, Journal of Agricultural Machinery Science, 7(4), pp. 399–403. Available at: https://dergipark.org.tr/download/article-
file/118933.
Bochtis, D. and Sørensen, C. G. (2014) ‘Special Issue: Operations management - Operations Management in Bio-production Systems.’, Operations Management in Bio-production Systems, 120, pp. 1–116. Available at: https://www.sciencedirect.com/journal/biosystems-engineering/vol/120/suppl/C.
Bogunovic, I. et al. (2018) ‘Tillage management impacts on soil compaction , erosion and crop yield in Stagnosols (Croatia)’, Catena. Elsevier, 160(September 2017), pp. 376–384. doi: 10.1016/j.catena.2017.10.009.
Bonfil, D. J. et al. (2008) ‘On-combine near infrared spectroscopy applied to prediction of grain test weight’, in 9th International Conference on Precision Agriculture, pp. 1–8. Available at: https://www.ispag.org/proceedings/?action=abstract&id=2525&title=On-combine+Near+Infrared+Spectroscopy+Applied+to+Prediction+of+Grain+Test+Weight.
Braunack, M. V. and Dexter, A. R. (1989) ‘Soil Aggregation in the Seedbed: a Review. II. Effect of Aggregate Sizes on Plant Growth’, Soil and Tillage Research, 14, pp. 281–298.
Brewster, C. et al. (2017) ‘IoT in Agriculture: Designing a Europe-Wide Large-Scale Pilot’, IEEE Communications Magazine, 55(9), pp. 26–33. doi: 10.1109/MCOM.2017.1600528.
Brinkhoff, J. et al. (2017) ‘WiField , an IEEE 802 . 11-based Agricultural Sensor Data Gathering and Logging Platform’, in Eleventh International Conference on Sensing Technology (ICST).
Bünemann, E. K. et al. (2018) ‘Soil quality – A critical review’, Soil Biology and Biochemistry. Elsevier, 120(February), pp. 105–125. doi: 10.1016/j.soilbio.2018.01.030.
Burton, L. et al. (2018) ‘Smart Gardening IoT Soil Sheets for Real-Time Nutrient Analysis’, Journal of the Electrochemical Society, 165(8), pp. 3157–3162. doi: 10.1149/2.0201808jes.
Busato, P., Berruto, R. and Saunders, C. (2007) ‘Optimal Field-Bin Locations and Harvest Patterns to Improve the Combine Field Capacity : Study with a Dynamic ...’, in CIOSTA 07 001. Vol. IX. Agricultural Engineering International: the CIGR Ejournal. Available at: https://ecommons.cornell.edu/handle/1813/10619.
Cadavid, H. et al. (2018) ‘Towards a Smart Farming Platform : From IoT-Based Crop Sensing’, in Colombian Conference on Computing CCC 2018, Communications and Information Science CCIS, vol 885, pp. 237–251. doi: 10.1007/978-3-319-98998-3.
Caspersen, O. H. and Andersen, P. K. N. (2016) Udvikling i Agerlandet 1954-2025 – Kortlægning af markstørrelse, markveje og småbiotoper.
CEMA (2017) Digital Farming: what does it really mean?, CEMA aisbl - European Agricultural Machinery. Available at: http://cema-agri.org/sites/default/files/CEMA_Digital Farming - Agriculture 4.0_ 13 02 2017.pdf (Accessed: 22 March 2018).
Çerçioğlu, M. et al. (2019) ‘Geoderma Effect of cover crop management on soil hydraulic properties’, Geoderma. Elsevier, 343(March), pp. 247–253. doi: 10.1016/j.geoderma.2019.02.027.
Cerdeira-pena, A., Carpente, L. and Amiama, C. (2017) ‘Optimised forage harvester routes as solutions to a traveling salesman problem with clusters and time windows’, Biosystems Engineering. Elsevier Ltd, 164, pp. 110–123. doi: 10.1016/j.biosystemseng.2017.10.002.
Chamen, T. et al. (2003) ‘Prevention strategies for field traffic-induced subsoil compaction : a review Part 2 . Equipment and field practices’, Soil and Tillage Research, 73, pp. 161–174. doi: 10.1016/S0167-1987(03)00108-9.
Chamen, T. (2015) ‘The Potential of Controlled Traffic Farming to Mitigate Greenhouse Gas Emissions and Enhance Carbon Sequestration in Arable Land: A Critical Review’, Transactions of the ASABE, (June), pp. 707–731. doi: 10.13031/trans.58.11049.
Chatzikostas, G. et al. (2019) Smart Agri Hubs D3.1 Innovation Experiment Guidelines. Available at: https://smartagrihubs.eu/Deliverables/pdfs/D3.1_IE Guidelines_final.pdf.
Chen, G. and Weil, R. R. (2011) ‘Root growth and yield of maize as affected by soil compaction and cover crops’, Soil & Tillage Research. Elsevier B.V., 117, pp. 17–27. doi: 10.1016/j.still.2011.08.001.
Christensen, S. et al. (2009) ‘Site-specific weed control technologies’, Weed Research, 49, pp. 233–241. doi: 10.1111/j.1365-3180.2009.00696.x.
Christiansen, P. et al. (2016) ‘DeepAnomaly : Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field’, Sensors, 16(1904), pp. 1–21. doi: 10.3390/s16111904.
Clarke, G. and Wright, J. R. (1964) ‘Scheduling of Vehicle Routing Problem from a Central Depot to a Number of Delivery Points’, Operations Research, 12, pp. 568–581. doi: 10.1287/opre.12.4.568.
Conesa-Muñoz, J., Pajares, G. and Ribeiro, A. (2016) ‘Mix-opt: A new route operator for optimal coverage path planning for a fleet in an agricultural environment’, Expert Systems with Applications, 54, pp. 364–378. doi: 10.1016/j.eswa.2015.12.047.
Costa, C. et al. (2013) ‘A Review on Agri-food Supply Chain Traceability by Means of RFID Technology’, Food and Bioprocess Technology, 6(2), pp. 353–366. doi: 10.1007/s11947-012-0958-7.
Crist, E., Mora, C. and Engelman, R. (2017) ‘The interaction of human population, food production, and biodiversity protection’, Science, 356(November), pp. 260–264. doi: 10.1126/science.aal2011.
Czechlowski, M. and Wojciechowski, T. (2013) ‘The utilization of information about local variable environmental conditions to predict the quality of wheat grain during the harvest’, Journal of Research and Applications in Agricultural Engineering, 58(1), pp. 31–34. Available at: https://www.researchgate.net/publication/249312990.
D’Este, P. et al. (2019) ‘The relationship between interdisciplinarity and distinct modes of university-industry interaction’, Research Policy. Elsevier, 48(9), p. 103799. doi: 10.1016/j.respol.2019.05.008.
Dantzig, G. B. and Ramser, J. H. (1959) ‘The Truck Dispatching Problem’, Management Science, 6(1), pp. 80–91. doi: 10.1287/mnsc.6.1.80.
Day, W. (2011) ‘Engineering advances for input reduction and systems management to meet the challenges of global food and farming futures’, The Journal of Agricultural Science, 149(S1), pp. 55–61. Available at: http://www.journals.cambridge.org/abstract_S002185961000095X.
Dhall, R. and Agrawal, H. (2018) ‘An Improved Energy Efficient Duty Cycling Algorithm for IoT based Precision Agriculture’, Procedia Computer Science. Elsevier B.V., 141, pp. 135–142. doi: 10.1016/j.procs.2018.10.159.
Dhinari, L. L. et al. (2017) ‘Cloud Computing and Internet of Things Fusion: Cost Issues’, in Eleventh International Conference on Sensing Technology (ICST), pp. 2–7.
Diedrichs, A. L. et al. (2018) ‘Prediction of Frost Events Using Machine Learning and IoT Sensing Devices’, IEEE Internet of Things Journal. IEEE, 5(6), pp. 4589–4597. doi: 10.1109/JIOT.2018.2867333.
Djelveh, S. and Bisevac, V. (2016) D3.7. Smart-AKIS Policy Gaps and Briefs. Available at: https://www.smart-akis.com/wp-content/uploads/2018/08/SmartAKIS_D3.7_Final.pdf.
DLG (2017) DLG belønner igen højere proteinindhold i korn. Available at: https://www.dlg.dk/Om-DLG/Presse/Nyheder/2017/03/DLG-beloenner-igen-hoejere-proteinindhold-i-korn (Accessed: 12 May 2021).
Edwards, G. et al. (2015a) ‘Optimised schedules for sequential agricultural operations using a Tabu Search method’, Computers and Electronics in Agriculture. Elsevier B.V., 117, pp. 102–113. doi: 10.1016/j.compag.2015.07.007.
Edwards, G. et al. (2015b) ‘Optimised schedules for sequential agricultural operations using a Tabu Search method’, Computers and Electronics in Agriculture, 117, pp. 102–113. doi: 10.1016/j.compag.2015.07.007.
Edwards, G. et al. (2016) ‘Modelling the readiness of soil for different methods of tillage’, Soil and Tillage Research, 155, pp. 339–350. doi: 10.1016/j.still.2015.08.013.
Edwards, G., Bochtis, D. and Søresen, C. G. (2013) ‘Multi-machine coordination: Scheduling operations based on readiness criteria and using a modified tabu search algorithm’, IFAC Proceedings Volumes (IFAC-PapersOnline), 4(PART 1), pp. 191–195. doi: 10.3182/20130828-2-SF-3019.00023.
Edwards, G. T. C. (2015) PhD Thesis: Field readiness and operation scheduling, Department of Mechanical Engineering. Aarhus University, Denmark.
Edwards, G. T. C. et al. (2016) ‘Assessing the actions of the farm managers to execute field operations at opportune times’, Biosystems Engineering. Elsevier Ltd, 144, pp. 38–51. doi: 10.1016/j.biosystemseng.2016.01.011.
Edwards, G. T. C. et al. (2017) ‘Route Planning Evaluation of a Prototype Optimised Infield Route Planner for Neutral Material Flow Agricultural Operations’, Biosystems Engineering, 153, pp. 149–157. doi: 10.1016/j.biosystemseng.2016.10.007.
Elijah, O., Member, S. and Rahman, T. A. (2018) ‘An Overview of Internet of Things ( IoT ) and Data Analytics in Agriculture : Benefits and Challenges’, IEEE Internet of Things Journal. IEEE, 5(5), pp. 3758–3773. doi: 10.1109/JIOT.2018.2844296.
Enemark, S. and Sørensen, E. M. (2016) ‘Deregulation of the Agricultural Sector in Denmark’, in Scaling Up Responsible Land Governance, Annual World bank Conference on Land and Poverty. March 14-18. Washington DC: World Bank Publications, pp. 1–7.
Estrada-lópez, J. J. et al. (2018) ‘Smart Soil Parameters Estimation System Using an Autonomous Wireless Sensor Network With Dynamic Power Management Strategy’, Sensors Journal, 18(21), pp. 8913–8923. doi: 10.1109/JSEN.2018.2867432.
FAO (2011) Global food losses and food waste – Extent, causes and prevention, Food Loss and Food Waste: Causes and Solutions. Rome. doi: 10.4337/9781788975391.
Faraci, G. et al. (2018) ‘A 5G platform for Unmanned Aerial Monitoring in Rural Areas: Design and Performance Issues’, in IEEE International Conference on Network Softwarization (NetSoft 2018) - Technical Sessions. IEEE, pp. 237–241. doi: 10.1109/NETSOFT.2018.8459960.
Farquharson, R. J. (2006) ‘Production response and input demand in decision making : nitrogen fertilizer and wheat growers’, Australasian Agribusiness Review, 14(January 2006), pp. 1–14. doi: 10.22004/ag.econ.126105.
Ferrández-Pastor, F. et al. (2016) ‘Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture’, Sensors, 16(8), p. 1141. doi: 10.3390/s16071141.
Ferrández-Pastor, F. J. et al. (2018) ‘Precision Agriculture Design Method Using a Distributed Computing Architecture on Internet of Things Context’, Sensors, 18(1731), pp. 1–21. doi: 10.3390/s18061731.
Ferreira, D. et al. (2017) ‘Towards Smart Agriculture using FIWARE Enablers’, in 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC). Funchal, Portugal: IEEE, pp. 1544–1551. doi: 10.1109/ICE.2017.8280066.
Foldager, F. F. et al. (2018) ‘Design Space Exploration in the Development of Agricultural Robots’, in Proceedings of the EurAgEng 2018. Wageningen, The Netherlands, pp. 314–321.
Foley, J. A. et al. (2011) ‘Solutions for a cultivated planet’, Nature. Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved., 478(7369), pp. 337–342. Available at: http://dx.doi.org/10.1038/nature10452.
Fountas, S., Sørensen, C. G., et al. (2015) ‘Farm machinery management information system’, Computers and Electronics in Agriculture. Elsevier B.V., 110, pp. 131–138. doi: 10.1016/j.compag.2014.11.011.
Fountas, S., Carli, G., Sørensen, C. G., et al. (2015) ‘Farm management information systems: Current situation and future perspectives’, Computers and Electronics in Agriculture. Elsevier B.V., 115, pp. 40–50. doi: 10.1016/j.compag.2015.05.011.
Fountas, S., Carli, G., Sørensen, C.G., et al. (2015) ‘Farm management information systems: Current situation and future perspectives’, Computers and Electronics in Agriculture. Elsevier B.V., 115, pp. 40–50. doi: 10.1016/j.compag.2015.05.011.
Fronzek, S. et al. (2018) ‘Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change’, Agricultural Systems, 159, pp. 209–224. doi: 10.1016/j.agsy.2017.08.004.
Gao, C. and Yao, K. (2016) ‘The Design and Implementation of Portable Agricultural Microclimate Data Acquisition System Based on Android Platform’, Proceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015, 1, pp. 210–213. doi: 10.1109/ISCID.2015.275.
Gasso, V. et al. (2013) ‘Controlled traffic farming: A review of the environmental impacts’, European Journal of Agronomy, 48, pp. 66–73.
Gill, S. S., Chana, I. and Buyya, R. (2017) ‘IoT Based Agriculture as a Cloud and Big Data Service’, Journal of Organizational and End User Computing, 29(4), pp. 1–23. doi: 10.4018/JOEUC.2017100101.
Giordano, S. et al. (2018) ‘IoT Solutions for Crop Protection against Wild Animal Attacks’, in 2018 IEEE International Conference on Environmental Engineering (EE). IEEE, pp. 1–5. doi: 10.1109/EE1.2018.8385275.
Goap, A. et al. (2018) ‘An IoT based smart irrigation management system using Machine learning and open source
technologies’, Computers and Electronics in Agriculture. Elsevier, 155(May), pp. 41–49. doi: 10.1016/j.compag.2018.09.040.
Godfray, H. C. J. et al. (2010) ‘Food Security : The Challenge of Feeding 9 Billion People’, Science, 327(5967), pp. 812–818. doi: DOI: 10.1126/science.1185383.
Godwin, R. J. and Miller, P. C. H. (2003) ‘A review of the technologies for mapping within-field variability’, Biosystems Engineering, 84(4), pp. 393–407.
Golden, B. L., Assad, A. A. and Wasil, E. A. (2002) ‘Routing Vehicles in the Real World: Applications in the Solid Waste, Beverage, Food, Dairy, and Newspaper Industries’, in Toth, P. and Vigo, D. (eds) The Vehicle Routing Problem. Bologna, Italy: SIAM publications, pp. 245–286. doi: 10.1137/1.9780898718515.ch10.
Goldstein, A., Fink, L. and Meitin, A. (2018) ‘Applying machine learning on sensor data for irrigation recommendations : revealing the agronomist ’ s tacit knowledge’, Precision Agriculture. Springer US, 19(3), pp. 421–444. doi: 10.1007/s11119-017-9527-4.
Gorter, N. (2019) Route optimization of primary and service units in agricultural harvesting operations. Wageningen University and Research Centre. Available at: https://edepot.wur.nl/504244.
Green, O. et al. (2009) ‘Monitoring and modeling temperature variations inside silage stacks using novel wireless sensor networks’, Computer and Electronic in Agriculture, 69, pp. 149–157. doi: 10.1016/j.compag.2009.07.021.
Green, O. et al. (2014) ‘Commercial Autonomous Agricultural Platform - Kongskilde Robotti’, in Proceedings of the Second International Conference on Robotics, Associated High-Technologies and Equipment for Agriculture and Forestry - RHEA 2014: New trends in mobile robotics, perception and actuation for agriculture and forestry. Madrid, pp. 351–356.
Griffin, T. W. et al. (2008) ‘Spatial analysis of yield monitor data: Case studies of on-farm trials and farm management decision making’, Precision Agriculture, 9(5), pp. 269–283. doi: 10.1007/s11119-008-9072-2.
Guerrero, A., Neve, S. De and Mouazen, A. M. (2021) ‘Data fusion approach for map-based variable-rate nitrogen fertilization in barley and wheat’, Soil & Tillage Research. Elsevier B.V., 205(April 2020), p. 104789. doi: 10.1016/j.still.2020.104789.
Gyldengren, J. G. et al. (2020) ‘Effects of winter wheat N status on assimilate and N partitioning in the mechanistic agroecosystem model DAISY’, Journal of Agronomy and Crop Science, 206(6), pp. 784–805. doi: 10.1111/jac.12412.
Håkansson, I. and Reeder, R. C. (1994) ‘Subsoil compaction by vehicles with high axle load-extent, persistence and crop response’, Soil and Tillage Research, 29, pp. 277–304.
Håkansson, I., Voorhees, W. . B. and Riley, H. (1988) ‘Vehicle and wheel factors influencing soil compaction and crop response in different traffic regimes’, Soil and Tillage Research, 11, pp. 239–282.
Hamrita, T. K. and Hoffacker, E. C. (2005) ‘Development of a “smart” wireless soil monitoring sensor prototype using RFID technology’, Applied Engineering in Agriculture, 21(1), pp. 139–143. doi: 10.13031/2013.17904.
Hamza, M. A. and Anderson, W. K. (2005) ‘Soil compaction in cropping systems: A review of the nature, causes and possible solutions’, Soil and Tillage Research, 82(2), pp. 121–145. doi: https://doi.org/10.1016/j.still.2004.08.009.
Havlin, J. L. and Heiniger, R. W. (2009) ‘A variable-rate decision support tool’, Precision Agriculture, 10(April), pp. 356–369. doi: 10.1007/s11119-009-9121-5.
He, P. and Li, J. (2019) ‘The two-echelon multi-trip vehicle routing problem with dynamic satellites for crop harvesting and transportation’, Applied Soft Computing Journal. Elsevier B.V., 77, pp. 387–398. doi: 10.1016/j.asoc.2019.01.040.
Hernandez-Rojas, D. et al. (2018) ‘IoT Android Gateway for Monitoring and Control a WSN’, in Botto-Tobar, M. et al. (eds) CITT 2017: Technology Trends. Communications in Computer and Information Science. Springer, Cham, pp. 18–32. doi: 10.1007/978-3-319-72727-1_2.
Hernández-rojas, D. L. et al. (2018) ‘Design and Practical Evaluation of a Family of Lightweight Protocols for Heterogeneous Sensing through BLE Beacons in IoT Telemetry Applications’, Sensors, 18(1), pp. 1–33. doi: 10.3390/s18010057.
Higgins, V. et al. (2017) ‘Ordering adoption: Materiality, knowledge and farmer engagement with precision agriculture technologies’, Journal of Rural Studies. Elsevier Ltd, 55, pp. 193–202. doi: 10.1016/j.jrurstud.2017.08.011.
Horn, R., Way, T. and Rostek, J. (2003) ‘Effect of repeated tractor wheeling on stress/strain properties and consequences on physical properties in structured arable soils’, Soil & Tillage Research, 73(1–2), pp. 101–106. doi: 10.1016/S0167-1987(03)00103-X.
Hsion, C. et al. (2021) ‘Harvesting and evacuation route optimisation model for fresh fruit bunch in the oil palm plantation site’, Journal of Cleaner Production. Elsevier Ltd, 307, p. 127238. doi: 10.1016/j.jclepro.2021.127238.
Huang, J. and Zhang, L. (2017) ‘The big data processing platform for intelligent agriculture’, AIP Conference Proceedings, 1864. doi: 10.1063/1.4992850.
Jain, P. et al. (2018) ‘Development of an Energy-efficient Adaptive IoT Gateway Model for Precision Agriculture’, in 2018 Global Internet of Things Summit (GIoTS). IEEE, pp. 1–6. doi: 10.1109/GIOTS.2018.8534553.
El Jarroudi, Moussa et al. (2017) ‘Improving fungal disease forecasts in winter wheat: A critical role of intra-day variations of meteorological conditions in the development of Septoria leaf blotch’, Field Crops Research. Elsevier, 213(August), pp. 12–20. doi: 10.1016/j.fcr.2017.07.012.
Jawad, H. et al. (2017) ‘Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review’, Sensors, 17(8), p. 1781. doi: 10.3390/s17081781.
Jayaraman, P. P. et al. (2015) ‘Addressing Information Processing Needs of Digital Agriculture with OpenIoT Platform’, in Podnar Žarko, I., Pripužić, K., and Serrano, M. (eds) Interoperability and Open-Source Solutions for the Internet of Things. Lecture Notes in Computer Science. Springer, Cham, pp. 137–152. doi: 10.1007/978-3-319-16546-2_11.
Jayaraman, P. P. et al. (2016) ‘Internet of things platform for smart farming: Experiences and lessons learnt’, Sensors (Switzerland), 16(11), pp. 1–17. doi: 10.3390/s16111884.
Jayashankar, P. et al. (2018) ‘IoT adoption in agriculture : the role of trust , perceived value and risk’, Journal of Business & Industrial Marketing, 33(6), pp. 804–821. doi: 10.1108/JBIM-01-2018-0023.
Jensen, M. A. F. et al. (2012) ‘In-field and inter-field path planning for agricultural transport units’, Computers and Industrial Engineering, 63(4), pp. 1054–1061. doi: 10.1016/j.cie.2012.07.004.
Jensen, M. F. et al. (2015) ‘Coverage planning for capacitated field operations, Part I: Task decomposition’, Biosystems Engineering, 139(2009), pp. 136–148. doi: 10.1016/j.biosystemseng.2015.07.003.
Jensen, M. F., Bochtis, D. and Sørensen, C. G. (2015) ‘Coverage planning for capacitated field operations , part II : Optimisation’, Biosystems Engineering. Elsevier Ltd, 139, pp. 149–164. doi: 10.1016/j.biosystemseng.2015.07.002.
Jørgensen, Johannes Ravn (2001) ‘Kvalitet af hvede til produktion af brød og andre produkter’, in Waagepetersen, J. et al. (eds) Produktion af kvalitetshvede i Danmark, en oversigt over problemer og muligheder. DJF rapport Markbrug, no. 53. Danish Institute of Agricultural Sciences , Department of Plant Biology, pp. 25–32. Available at: http://www.agrsci.dk/djfpublikation/index.asp?action=show&id=550.
Joshi, N. et al. (2016) ‘A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring’, pp. 1–23. doi: 10.3390/rs8010070.
Juul, J. P., Green, O. and Jacobsen, R. H. (2015) ‘Deployment of Wireless Sensor Networks in Crop Storages’, Wireless Personal Communications, 81, pp. 1437–1454. doi: 10.1007/s11277-015-2482-3.
Kaloxylos, A. et al. (2012) ‘Farm management systems and the Future Internet era’, Computers and Electronics in Agriculture. Elsevier B.V., 89, pp. 130–144. doi: 10.1016/j.compag.2012.09.002.
Kaloxylos, A. et al. (2014) ‘A cloud-based farm management system: Architecture and implementation’, Computers and Electronics in Agriculture. Elsevier B.V., 100, pp. 168–179. Available at: http://dx.doi.org/10.1016/j.compag.2013.11.014.
Kamarudin, L. M., Ahmad, R. B. and Ndzi, D. L. (2016) ‘Simulation and analysis of LEACH for wireless sensor networks in agriculture Ammar Zakaria and Kamarulzaman Kamarudin Mohamed Elshaikh Elobaid Said Ahmed’, 21(1), pp. 16–26.
Kamilaris, A. et al. (2016) ‘Agri-IoT: A semantic framework for Internet of Things-enabled smart farming applications’, 2016 IEEE 3rd World Forum on Internet of Things, WF-IoT 2016, pp. 442–447. doi: 10.1109/WF-IoT.2016.7845467.
Kamilaris, A., Kartakoullis, A. and Prenafeta-Boldú, F. X. (2017) ‘A review on the practice of big data analysis in
agriculture’, Computers and Electronics in Agriculture. Elsevier, 143(January), pp. 23–37. doi: 10.1016/j.compag.2017.09.037.
Kassal, P., Steinberg, M. D. and Murkovi, I. (2018) ‘Chemical Wireless chemical sensors and biosensors : A review’, Sensors and Actuators B, 266, pp. 228–245. doi: 10.1016/j.snb.2018.03.074.
Kayacan, Erkan et al. (2015) ‘Towards agrobots : Identification of the yaw dynamics and trajectory tracking of an autonomous tractor’, Computers and Electronics in Agriculture. Elsevier B.V., 115, pp. 78–87. doi: 10.1016/j.compag.2015.05.012.
Keller, T. et al. (2004) ‘Soil precompression stress: II. A comparison of different compaction tests and stress-displacement behaviour of the soil during wheeling’, Soil and Tillage Research, 77(1), pp. 97–108. doi: 10.1016/j.still.2003.11.003.
Keller, T. et al. (2019) ‘Historical increase in agricultural machinery weights enhanced soil stress levels and adversely affected soil functioning’, Soil & Tillage Research. Elsevier, 194(January), p. 104293. doi: 10.1016/j.still.2019.104293.
Keller, T. and Arvidsson, J. (2004) ‘Technical solutions to reduce the risk of subsoil compaction: effects of dual wheels , tandem wheels and tyre inflation pressure on stress propagation in soil’, Soil & Tillage Research, 79, pp. 191–205. doi: 10.1016/j.still.2004.07.008.
Keller, T. and Arvidsson, J. (2016) ‘A model for prediction of vertical stress distribution near the soil surface below rubber-tracked undercarriage systems fi tted on agricultural vehicles’, Soil & Tillage Research. Elsevier B.V., 155, pp. 116–123. doi: 10.1016/j.still.2015.07.014.
Khanal, S., Fulton, J. and Shearer, S. (2017) ‘An overview of current and potential applications of thermal remote sensing in precision agriculture’, Computers and Electronics in Agriculture, 139, pp. 22–32. doi: 10.1016/j.compag.2017.05.001.
Khattab, A., Abdelgawad, A. and Khattab, A. (2016) ‘Design and implementation of a cloud-based IoT scheme for precision agriculture Design and Implementation of a Cloud-based IoT Scheme for Precision Agriculture’, (December), pp. 10–14. doi: 10.1109/ICM.2016.7847850.
Kitchen, N. R. and Roger, R. D. (2007) ‘Emerging technologies for real-time and integrated agriculture decisions’, Computer and Electronic in Agriculture, 61, pp. 1–3. doi: 10.1016/j.compag.2007.06.007.
Klaina, H. et al. (2018) ‘Narrowband Characterization of Near-Ground Radio’, Sensors, 18(2428), pp. 1–15. doi: 10.3390/s18082428.
Kodali, R. K., Jain, V. and Karagwal, S. (2017) ‘IoT based smart greenhouse’, in IEEE Region 10 Humanitarian Technology Conference 2016, R10-HTC 2016 - Proceedings. doi: 10.1109/R10-HTC.2016.7906846.
Kodali, R. K. and Sahu, A. (2016) ‘An IoT based soil moisture monitoring on Losant platform’, Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016, pp. 764–768. doi: 10.1109/IC3I.2016.7918063.
Köksal, Ö. and Tekinerdogan, B. (2018) ‘Architecture design approach for IoT ‑ based farm management information systems’, Precision Agriculture. Springer US, pp. 1–33. doi: 10.1007/s11119-018-09624-8.
Koleva, K. and Toteva-Lyutova, P. (2018) ‘Greenhouses automation as an illustration of interdisciplinarity in the creation of technical innovations’, Procedia Manufacturing. Elsevier B.V., 22, pp. 923–930. doi: 10.1016/j.promfg.2018.03.131.
Kruize, J. W. et al. (2016) ‘Original papers A reference architecture for Farm Software Ecosystems’, Computers and Electronics in Agriculture. Elsevier B.V., 125, pp. 12–28. doi: 10.1016/j.compag.2016.04.011.
Lamsal, K., Jones, P. C. and Thomas, B. W. (2016) ‘Computers & Industrial Engineering Harvest logistics in agricultural systems with multiple , independent producers and no on-farm storage’, Computers & Industrial Engineering. Elsevier Ltd, 91, pp. 129–138. doi: 10.1016/j.cie.2015.10.018.
Langendoen, K., Baggio, A. and Visser, O. (2006) ‘Murphy Loves Potatoes: Experiences from a Pilot Sensor Network Deployment in Precision Agriculture’, in Proceedings 20th IEEE International Parallel & Distributed Processing Symposium. Rhodes Island, Greece: IEEE, pp. 1–8. doi: 10.1109/IPDPS.2006.1639412.
LBST (2021) Markkort og markblokke. Available at: https://lbst.dk/landbrug/kort-og-markblokke/markkort-og-markblokke/ (Accessed: 16 March 2021).
Leroux, C. and Tisseyre, B. (2018) ‘How to measure and report within-field variability : a review of common indicators and their sensitivity’, Precision Agriculture, 20(August), pp. 562–590. doi: 10.1007/s11119-018-9598-x.
LF, L. og F. (2020) Fakta om Fødevareklyngen 2020 -Bæredygtig Udvikling. doi: 978-87-87323-08-7.
Lipiec, J. et al. (2012) ‘Effects of soil compaction on root elongation and anatomy of different cereal plant species’, Soil & Tillage Research. Elsevier B.V., 121, pp. 74–81. doi: 10.1016/j.still.2012.01.013.
Lipiec, J. and Hatano, R. (2003) ‘Quantification of compaction effects on soil physical properties and crop growth’, Geoderma, 116, pp. 107–136. doi: 10.1016/S0016-7061(03)00097-1.
Long, D. S., Mccallum, J. D. and Scharf, P. A. (2013) ‘Optical-Mechanical System for On-Combine Segregation of Wheat by Grain Protein Concentration’, Agronomy Journal, 105(6), pp. 1529–1535. doi: 10.2134/agronj2013.0206.
López-Riquelme, J. A. et al. (2017) ‘A software architecture based on FIWARE cloud for Precision Agriculture’, Agricultural Water Management. Elsevier B.V., 183, pp. 123–135. doi: 10.1016/j.agwat.2016.10.020.
López-Riquelme, J.A. et al. (2017) ‘A software architecture based on FIWARE cloud for Precision Agriculture’, Agricultural Water Management. Elsevier B.V., 183, pp. 123–135. doi: 10.1016/j.agwat.2016.10.020.
Lyle, G., Bryan, B. A. and Ostendorf, B. (2014) ‘Post-processing methods to eliminate erroneous grain yield measurements: Review and directions for future development’, Precision Agriculture, 15(4), pp. 377–402. doi: 10.1007/s11119-013-9336-3.
Macleod, M. and Nagatsu, M. (2018) ‘What does interdisciplinarity look like in practice : Mapping interdisciplinarity and its limits in the environmental sciences’, Studies in History and Philosophy of Science. Elsevier Ltd, 67, pp. 74–84. doi: 10.1016/j.shpsa.2018.01.001.
Mafuta, M. et al. (2012) ‘Successful Deployment of a Wireless Sensor Network for Precision Agriculture in Malawi’, in 3rd International Conference on Networked Embedded Systems for Every Application (NESEA). IEEE, pp. 1–7.
Manap, H. and Najib, M. S. (2014) ‘Sensors and Actuators B : Chemical A DOAS system for monitoring of ammonia emission in the agricultural sector’, Sensors & Actuators: B. Chemical. Elsevier B.V., 205, pp. 411–415. doi: 10.1016/j.snb.2014.08.080.
Marín-González, O. et al. (2013) ‘On-line measurement of soil properties without direct spectral response in near infrared spectral range’, Soil and Tillage Research, 132, pp. 21–29. doi: 10.1016/j.still.2013.04.004.
Marsch, P. et al. (2016) ‘5G Radio Access Network Architecture – Design Guidelines and Key Considerations’, IEEE Communications Magazine, 54(11), pp. 24–32. doi: 10.1109/MCOM.2016.1600147CM.
Martin, C. T., Mccallum, J. D. and Long, D. S. (2013) ‘A Web-Based Calculator for Estimating the Profit Potential of Grain Segregation by Protein Concentration’, Agronomy Journal, 105(3), pp. 721–726. doi: 10.2134/agronj2012.0353.
Martínez, R. et al. (2016) ‘A testbed to evaluate the fiware-based iot platform in the domain of precision agriculture’, Sensors (Switzerland), 16(11). doi: 10.3390/s16111979.
Mäyrä, O. et al. (2018) ‘Plant Disease Outbreak – Prediction by Advanced Data Analysis’, SNE Short Note, 28(3), pp. 113–115. doi: 10.11128/sne.28.sn.10431.
Mazon-Olivo, B. et al. (2018) ‘Rules engine and complex event processor in the context of internet of things for precision agriculture’, Computers and Electronics in Agriculture. Elsevier, 154(February), pp. 347–360. doi: 10.1016/j.compag.2018.09.013.
McBratney, A. ., Mendonça Santos, M. . and Minasny, B. (2003) ‘On digital soil mapping’, Geoderma, 117(1–2), pp. 3–52. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0016706103002234.
McHugh, A. D., Tullberg, J. N. and Freebairn, D. M. (2009) ‘Controlled traffic farming restores soil structure’, Soil and Tillage Research, 104(1), pp. 164–172. doi: 10.1016/j.still.2008.10.010.
Meola, A. (2016) ‘Why IoT, big data & smart farming are the future of agriculture’, Business Insider - Dec 20, 2016, December. Available at: https://www.businessinsider.com/internet-of-things-smart-agriculture-2016-10?r=US&IR=T.
Meyer-Aurich, A. et al. (2008) ‘Economic analysis of site-specific wheat management with respect to grain quality and separation of the different quality fractions’, in 12th Congress of European Association of Agricultural Economists - EAAE 2008. Ghent, Belgium, pp. 1–7. doi: 10.22004/ag.econ.43649.
Midtiby, H. S., Steen, K. A. and Green, O. (2018) ‘In row cultivation controlled by plant patterns’, Computers and Electronics in Agriculture. Elsevier, 153(July), pp. 62–68. doi: 10.1016/j.compag.2018.07.037.
Miettinen, M. et al. (2006) ‘Fault Diagnosis in Agricultural Machines’, in ASABE International Conference on Automation Technology for Off-road Equipment. Bonn, Germany: ASABE, pp. 1–10. Available at: https://www.computoolable.com/AgF4.pdf.
Moon, A. et al. (2018) ‘Evaluating fi delity of lossy compression on spatiotemporal data from an IoT enabled smart farm’, Computers and Electronics in Agriculture. Elsevier, 154(March), pp. 304–313. doi: 10.1016/j.compag.2018.08.045.
Moysiadis, V. et al. (2020) ‘Mobile Robotics in Agricultural Operations : A Narrative Review on Planning Aspects’, Applied Sciences, 10, p. 3453. doi: 10.3390/app10103453.
Mukherjee, A. et al. (2018) ‘Blind Entity Identification for Agricultural IoT Deployments’, Internet of Things Journal. IEEE, pp. 1–8. doi: 10.1109/JIOT.2018.2879454.
Munkholm, L. J., Schjønning, P. and Rüegg, K. (2005) ‘Mitigation of subsoil recompaction by light traffic and on-land ploughing I . Soil response’, Soil & Tillage Research, 80, pp. 149–158. doi: 10.1016/j.still.2004.03.015.
Na, A. and Isaac, W. (2016) ‘Developing a human-centric agricultural model in the IoT environment’, 2016 International Conference on Internet of Things and Applications, IOTA 2016, pp. 292–297. doi: 10.1109/IOTA.2016.7562740.
Nakutis, Z. et al. (2016) ‘Remote Agriculture Automation Using Wireless Link and IoT Gateway Infrastructure’, Proceedings - International Workshop on Database and Expert Systems Applications, DEXA, 2016-Febru, pp. 99–103. doi: 10.1109/DEXA.2015.37.
Näsi, R. et al. (2018) ‘Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric Features’, Remote Sensing, 10(7)(1082), pp. 1–32. doi: 10.3390/rs10071082.
Neve, S. De and Hofman, G. (2000) ‘Influence of soil compaction on carbon and nitrogen mineralization of soil organic matter and crop residues’, Biol. Fertil. Soils, 30, pp. 544–549. doi: https://doi.org/10.1007/s003740050.
Niedbała, G., Czechlowski, M. and Wojciechowski, T. (2013) ‘The use of artificial neural networks to predict the spatial variability of grain quality during combine harvest of wheat’, Journal of Research and Applications in Agricultural Engineering, 58(1), pp. 126–129. Available at: https://www.researchgate.net/publication/249313171.
Nielsen, S. K. et al. (2017) ‘Seed drill instrumentation for spatial coulter depth measurements’, Computers and Electronics in Agriculture, 141, pp. 207–214.
Nilsson, R. S. and Zhou, K. (2020) ‘Decision Support Tool for Operational Planning of Field Operations’, Agronomy Journal, 10(2), p. 229. doi: 10.3390/agronomy10020229.
O’Grady, M. J. and O’Hare, G. M. P. (2017) ‘Modelling the smart farm’, Information Processing in Agriculture. China Agricultural University, 4(3), pp. 179–187. doi: 10.1016/j.inpa.2017.05.001.
Obour, P. B., Keller, T., Lamandé, M., et al. (2019) ‘Pore structure characteristics and soil workability along a clay gradient’, Geoderma. Elsevier, 337(July 2018), pp. 1186–1195. doi: 10.1016/j.geoderma.2018.11.032.
Obour, P. B., Keller, T., Jensen, J. L., et al. (2019) ‘Soil water contents for tillage : A comparison of approaches and consequences for the number of workable days’, Soil & Tillage Research. Elsevier, 195(January), p. 104384. doi: 10.1016/j.still.2019.104384.
Okayasu, T. et al. (2017) ‘Affordable Field Environmental Monitoring and Plant Growth Measurement System for Smart Agriculture’, in Eleventh International Conference on Sensing Technology (ICST), pp. 7–10.
Oksanen, T. (2013) ‘Shape-describing indices for agricultural field plots and their relationship to operational efficiency’, Computers and Electronics in Agriculture. Elsevier B.V., 98, pp. 252–259. doi: 10.1016/j.compag.2013.08.014.
Oksanen, T., Linkolehto, R. and Seilonen, I. (2016) ‘Adapting an industrial automation protocol to remote monitoring of mobile agricultural machinery: a combine harvester with IoT’, in IFAC-PapersOnLine 49-16. Elsevier, pp. 127–131.
Oksanen, T., Piirainen, P. and Seilonen, I. (2015) ‘Remote access of ISO 11783 process data by using OPC Unified Architecture technology’, Computers and Electronics in Agriculture. Elsevier B.V., 117, pp. 141–148. doi: 10.1016/j.compag.2015.08.002.
Oksanen, T. and Visala, A. (2009) ‘Coverage path planning algorithms for agricultural field machines’, Journal of Field Robotics, 26(8), pp. 651–668. doi: 10.1002/rob.20300.
Orfanou, A. et al. (2013) ‘Scheduling for machinery fleets in biomass multiple-field operations’, Computers and Electronics in Agriculture, 94, pp. 12–19. doi: 10.1016/j.compag.2013.03.002.
Palosuo, T. et al. (2011) ‘Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models’, European Journal of Agronomy, 35(3), pp. 103–114. doi: https://doi.org/10.1016/j.eja.2011.05.001.
Paraforos, D. S. et al. (2016) ‘A Farm Management Information System Using Future Internet Technologies’, IFAC-PapersOnLine. Elsevier B.V., 49(16), pp. 324–329. doi: 10.1016/j.ifacol.2016.10.060.
Parry, D. W., Jenkinson, P. and McLeod, L. (1995) ‘Fusarium ear blight (scab) in small grain cereals—a review’, Plant Pathology, 44(2), pp. 207–238. doi: https://doi.org/10.1111/j.1365-3059.1995.tb02773.x.
Paul, P. A., Lipps, P. E. and Madden, L. V (2005) ‘Relationship between visual estimates of fusarium head blight intensity and deoxynivalenol accumulation in harvested wheat grain: a meta-analysis.’, Phytopathology. United States, 95(10), pp. 1225–1236. doi: 10.1094/PHYTO-95-1225.
Peets, S. et al. (2009) ‘RFID tags for identifying and verifying agrochemicals in food traceability systems’, Precision Agriculture, 10(5), pp. 382–394. doi: 10.1007/s11119-009-9106-4.
Peets, S. et al. (2012) ‘Methods and procedures for automatic collection and management of data acquired from on-the-go sensors with application to on-the-go soil sensors’, Computers and Electronics in Agriculture. Elsevier B.V., 81, pp. 104–112. doi: 10.1016/j.compag.2011.11.011.
Pelletier, N. (2008) ‘Environmental performance in the US broiler poultry sector : Life cycle energy use and greenhouse gas , ozone depleting , acidifying and eutrophying emissions’, Agricultural Systems, 98, pp. 67–73. doi: 10.1016/j.agsy.2008.03.007.
Pérez-Freire, L. and Brillouet, L. (2015) Smart Farming and Food Safety Internet of Things Applications - Challenges for Large Scale Implementations, AIOTI WG06. Available at: https://aioti.eu/wp-content/uploads/2017/03/AIOTIWG06Report2015-Farming-and-Food-Safety.pdf.
Pesonen, L. A. et al. (2014) ‘Cropinfra - An Internet-based service infrastructure to support crop production in future farms’, Biosystems Engineering. IAgrE, 120, pp. 92–101. doi: 10.1016/j.biosystemseng.2013.09.005.
Pfeiffer, D. and Blank, S. (2015) ‘Real-time Operator Performance Analysis in Agricultural Equipment. Understanding Unused Potential and Ways to Improve from Day to Day’, in 73rd International Conference on Agricultural Engineering, LANDTECHNIK AgEng 2015 Proceedings - Innovations in Agricultural Engineering for Efficient Farming. Hannover, Germany. Available at: https://www.researchgate.net/profile/Sebastian_Blank2/publication/283643214_Real-time_Operator_Performance_Analysis_in_Agricultural_Equipment/links/564c7d8e08aeab8ed5e9dcf4/Real-time-Operator-Performance-Analysis-in-Agricultural-Equipment.pdf.
Pham, X. and Stack, M. (2018) ‘How data analytics is transforming agriculture’, Business Horizons. ‘Kelley School of Business, Indiana University’, 61(1), pp. 125–133. doi: 10.1016/j.bushor.2017.09.011.
Pierpaoli, E. et al. (2013) ‘Drivers of Precision Agriculture Technologies Adoption : A Literature Review’, Procedia Technology. Elsevier B.V., 8(Haicta), pp. 61–69. doi: 10.1016/j.protcy.2013.11.010.
Ping, J. L. and Dobermann, A. (2005) ‘Processing of yield map data’, Precision Agriculture, 6(2), pp. 193–212. doi: 10.1007/s11119-005-1035-2.
Plessen, M. G. (2019) ‘Coupling of crop assignment and vehicle routing for harvest planning in agriculture’, Artificial Intelligence in Agriculture. Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd., 2, pp. 99–109. doi: 10.1016/j.aiia.2019.07.001.
Popović, T. et al. (2017) ‘Architecting an IoT-enabled platform for precision agriculture and ecological monitoring: A case study’, Computers and Electronics in Agriculture, 140, pp. 255–265. doi: 10.1016/j.compag.2017.06.008.
Pronin, D. et al. (2020) ‘Wheat (Triticum aestivum L.) Breeding from 1891 to 2010 Contributed to Increasing Yield and Glutenin Contents but Decreasing Protein and Gliadin Contents’, Journal of Agricultural and Food Chemistry. American Chemical Society, 68(46), pp. 13247–13256. doi: 10.1021/acs.jafc.0c02815.
Pulido-moncada, M., Munkholm, L. J. and Schjønning, P. (2019) ‘Wheel load, repeated wheeling, and traction effects on
subsoil compaction in northern Europe’, Soil & Tillage Research. Elsevier, 186(November 2018), pp. 300–309. doi: 10.1016/j.still.2018.11.005.
Punia, S., Singh, K. and Kumar, A. (2019) ‘Difference in protein content of wheat ( Triticum aestivum L .): Effect on functional , pasting , color and antioxidant properties’, Journal of the Saudi Society of Agricultural Sciences. King Saud University & Saudi Society of Agricultural Sciences, 18(4), pp. 378–384. doi: 10.1016/j.jssas.2017.12.005.
Ramundo, L., Taisch, M. and Terzi, S. (2016) ‘State of the art of technology in the food sector value chain towards the IoT’, 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), pp. 1–6. doi: 10.1109/RTSI.2016.7740612.
Ranjan, A. K. and Hussain, M. (2016) ‘Terminal Authentication in M2M Communications in the Context of Internet of Things’, Procedia Computer Science. The Author(s), 89, pp. 34–42. doi: 10.1016/j.procs.2016.06.006.
Raper, R. L. (2005) ‘Agricultural traffic impacts on soil’, Journal of Terramechanics, 42(3–4), pp. 259–280.
Ray, P. P. (2017) ‘Internet of things for smart agriculture: Technologies, practices and future direction’, Journal of Ambient Intelligence and Smart Environments, 9(4), pp. 395–420. doi: 10.3233/AIS-170440.
Ren, D. and Martynenko, A. (2018) ‘Guest editorial: Robotics and automation in agriculture’, International Journal of Robotics and Automation, 206(August), pp. 1–5. doi: 10.2316/Journal.206.2018.3.206-0001.
Renard, K. G. et al. (1991) ‘RUSLE: Revised Universal Soil Loss Equation’, J. Soil and Water Conservation, 46(1), pp. 30–33.
Renato Nunes, M. et al. (2018) ‘No-till and cropping system diversi fi cation improve soil health and crop yield’, Geoderma. Elsevier, 328(April), pp. 30–43. doi: 10.1016/j.geoderma.2018.04.031.
Reshma, S. R. J. and Pillai, A. S. (2018) ‘Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016)’, 614(SoCPaR 2016). doi: 10.1007/978-3-319-60618-7.
Richards, P. D. et al. (2012) ‘Exchange rates , soybean supply response , and deforestation in South America’, Global Environmental Change. Elsevier Ltd, 22(2), pp. 454–462. doi: 10.1016/j.gloenvcha.2012.01.004.
Risius, H. et al. (2015) ‘In-line estimation of falling number using near-infrared diffuse reflectance spectroscopy on a combine harvester’, Precision Agriculture, 16, pp. 261–274. doi: 10.1007/s11119-014-9374-5.
Risius, H., Hahn, J. and Korte, H. (2010) ‘Near Infrared Spectroscopy for Sorting Grain according to Specified Quality Parameters on a Combine Harvester’, in Book of Abstracts XVII.th World Congress of the International Commission of Agricultural and Biosystems Engineering (CIGR | SCGAB). Québec City: QC, Canada, p. 28. doi: 978-2-9811062-1-6.
Rodias, E. et al. (2017) ‘Energy Savings from Optimised In-Field Route Planning for Agricultural sustainability Energy Savings from Optimised In-Field Route Planning for Agricultural Machinery’, Sustainability, 9(1956), pp. 1–13. doi: 10.3390/su9111956.
Rodriguez, M. A., Cuenca, L. and Ortiz, A. (2018) ‘FIWARE Open Source Standard Platform in Smart Farming - A Review’, in Camarinha-Matos, L. M., Afsarmanesh, H., and Rezgui, Y. (eds) Collaborative Networks of Cognitive Systems 19th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2018. Cardiff, UK: Springer International Publishing, pp. 581–589. doi: 10.1007/978-3-319-99127-6.
Ruiz-Garcia, L. and Lunadei, L. (2011) ‘The role of RFID in agriculture: Applications, limitations and challenges’, Computers and Electronics in Agriculture, 79(1), pp. 42–50. doi: 10.1016/j.compag.2011.08.010.
Sabarina, K. and Priya, N. (2015) ‘Lowering data dimensionality in big data for the benefit of precision agriculture’, Procedia Computer Science. Elsevier Masson SAS, 48(C), pp. 548–554. doi: 10.1016/j.procs.2015.04.134.
Santoro, E., Soler, E. M. and Cherri, A. C. (2017) ‘Route optimization in mechanized sugarcane harvesting’, Computers and Electronics in Agriculture. Elsevier B.V., 141, pp. 140–146. doi: 10.1016/j.compag.2017.07.013.
Say, S. M. et al. (2017) ‘Adoption of Precision Agriculture Technologies in Developed and Developing Countries ADOPTION OF PRECISION AGRICULTURE TECHNOLOGIES’, in Isman, A. and Dündar, S. (eds) International Science and Technology Conference (ISTEC). Berlin, Germany, pp. 41–49. Available at: https://www.researchgate.net/publication/320908156_Adoption_of_Precision_Agriculture_Technologies_in_Developed_and_Developing_Countries.
Scheuren, S. et al. (2013) ‘Spatio-Temporally Constrained Planning for Cooperative Vehicles in a Harvesting Scenario’, KI - Künstliche Intelligenz, 27, pp. 341–346. doi: 10.1007/s13218-013-0267-y.
Schjønning, P. et al. (2008) ‘Modelling effects of tyre inflation pressure on the stress distribution near the soil – tyre interface’, Biosystems Engineering, 99, pp. 119–133. doi: 10.1016/j.biosystemseng.2007.08.005.
Schjønning, P. et al. (2015) ‘Driver-Pressure-State-Impact-Response (DPSIR) analysis and risk assessment for soil compaction-A European perspective’, Advances in Agronomy. Elsevier Ltd, 133, pp. 183–237.
Schjønning, P. et al. (2016) ‘Soil precompression stress, penetration resistance and crop yields in relation to differently-trafficked, temperate-region sandy loam soils’, Soil and Tillage Research. Elsevier, 163, pp. 298–308.
Schneider, F. et al. (2017) ‘Soil & Tillage Research The e ff ect of deep tillage on crop yield – What do we really know ?’, Soil & Tillage Research. Elsevier, 174(July), pp. 193–204. doi: 10.1016/j.still.2017.07.005.
Seehusen, T. et al. (2019) ‘Soil compaction and stress propagation after different wheeling intensities on a silt soil in South-East Norway’, Acta Agriculturae Scandinavica, Section B — Soil & Plant Science. Taylor & Francis, 69(4), pp. 343–355. doi: 10.1080/09064710.2019.1576762.
SEGES (2016) Præcisionsjordbrug i Danmark. Barriererapport: Identificering af udfordringer og forhold, der hæmmer udvikling, produktion og anvendelse af præcisionsjordbrugsteknikker i planteavlen. Available at: file:///C:/Users/AgroIntelli AVH/Downloads/Barriärrapportpraecisionsjordbrug-i-danmark.pdf.
SEGES (2021) Farmtalonine. Available at: www.landbrugsinfo.dk (Accessed: 12 May 2021).
Serrano, M. et al. (2015) Internet of Things IoT Semantic Interoperability: Research Challenges, Best Practices, Recommendations and Next Steps. Available at: http://www.eglobalmark.com/wp-content/uploads/2016/06/2015-03-IoT-Semantic-Interoperability-Research-Challenges-Best-Practices-Recommendations-and-Next-Steps.pdf.
Severino, G. et al. (2018) ‘The IoT as a tool to combine the scheduling of the irrigation with the geostatistics of the soils’, Future Generation Computer Systems, 82, pp. 268–273. doi: 10.1016/j.future.2017.12.058.
Seyyedhasani, H. and Dvorak, J. S. (2017) ‘Using the Vehicle Routing Problem to reduce field completion times with multiple machines’, Computers and Electronics in Agriculture. Elsevier B.V., 134, pp. 142–150. doi: 10.1016/j.compag.2016.11.010.
Seyyedhasani, H. and Dvorak, J. S. (2018) ‘Dynamic rerouting of a fleet of vehicles in agricultural operations through a Dynamic Multiple Depot Vehicle Routing Problem representation’, Biosystems Engineering. Elsevier Ltd, 171, pp. 63–77. doi: 10.1016/j.biosystemseng.2018.04.003.
Seyyedhasani, H., Dvorak, J. S. and Roemmele, E. (2019) ‘Routing algorithm selection for fi eld coverage planning based on fi eld shape and fl eet size’, Computers and Electronics in Agriculture. Elsevier, 156(December 2018), pp. 523–529. doi: 10.1016/j.compag.2018.12.002.
Simmonds, N. W. (1995) ‘The Relation Between Yield and Protein in Cereal Grain’, Journal of the Science of Food and Agriculture, 76(3), pp. 309–315. doi: 10.1002/jsfa.2740670306.
Sinha, R. S., Wei, Y. and Hwang, S. H. (2017) ‘A survey on LPWA technology: LoRa and NB-IoT’, ICT Express. Elsevier B.V., 3(1), pp. 14–21. doi: 10.1016/j.icte.2017.03.004.
Sjolander, A. J. et al. (2011) ‘Wireless tracking of cotton modules. Part 2: Automatic machine identification and system testing’, Computers and Electronics in Agriculture, 75(1), pp. 34–43. doi: 10.1016/j.compag.2010.09.015.
Skou-Nielsen, N. et al. (2017) ‘Creating a statistically representative set of Danish agricultural fi eld shapes to robustly test route planning algorithms’, in Precision Agriculture (ECPA) 2017, 8:2. Edinburgh, pp. 615–619. doi: 10.1017/S2040470017000188.
Sloth, N. M. and Poulsen, J. (2020) Næringsindhold i korn fra høsten 2020. Available at: https://svineproduktion.dk/-/media/PDF---Publikationer/Notater-2020/Notat_2026.ashx.
SmartAgriHubs (2021) Valued Grain Chain - from farm perspective. Available at: https://valuedgrainchain.eu/wp-content/uploads/2021/01/Valued-Grain-Chain-from-farm-perspective.pdf.
Sørensen, C. G. et al. (2010) ‘Conceptual model of a future farm management information system’, Computers and Electronics in Agriculture, 72(1), pp. 37–47. doi: 10.1016/j.compag.2010.02.003.
Sørensen, C. G. et al. (2011) ‘Functional requirements for a future farm management information system’, Computer and Electronic in Agriculture, 76, pp. 266–276. doi: 10.1016/j.compag.2011.02.005.
Sørensen, C. G. and Bochtis, D. D. (2010) ‘Conceptual model of fleet management in agriculture’, Biosystems
Engineering, 105(1), pp. 41–50.
Spekken, M. and Bruin, S. De (2013) ‘maneuvering and servicing time’, Precision Agriculture, 14, pp. 224–244. doi: 10.1007/s11119-012-9290-5.
Steen, K. A. et al. (2012) ‘Automatic Detection of Animals in Mowing Operations Using Thermal Cameras’, Sensors, 12(6), pp. 7587–7597. doi: 10.3390/s120607587.
Stettler, M. et al. (2014) ‘Terranimo® – a web-based tool for evaluating soil compaction’, Landtechnik, 69(3), pp. 132–138. doi: https://doi.org/10.15150/lt.2014.181.
Stewart, J., Stewart, R. and Kennedy, S. (2017) ‘Internet of Things - Propagation modelling for precision agriculture applications’, Wireless Telecommunications Symposium. doi: 10.1109/WTS.2017.7943528.
Stočes, M. et al. (2016) ‘Internet of Things (IoT) in Agriculture -Selected Aspects’, AGRIS on-line Papers in Economics and Informatics, 1(1), pp. 83–88. doi: 10.7160/aol.2016.080108.
Styczen, M. E. et al. (2020) ‘Analysis of the signi fi cant drop in protein content in Danish grain crops from 1990-2015 based on N-response in fertilizer trials’, European Journal of Agronomy. Elsevier, 115(July 2019), p. 126013. doi: 10.1016/j.eja.2020.126013.
Suhonen, J. et al. (2012) ‘Communication Protocols’, in Low-Power Wireless Sensor Networks Protocols, Services and Applications. 1st edn. New York: Springer-Verlag, pp. 27–41. doi: 10.1007/978-1-4614-2173-3.
Sundmaeker, H. et al. (2016) ‘Internet of Food and Farm 2020’, in Vermesan, O. and Friess, P. (eds) Digitising the Industry Internet of Things Connecting the Physical, Digital and Virtual Worlds. Gistrup, Denmark: River Publishers, pp. 1689–1699. doi: 10.1017/CBO9781107415324.004.
Talavera, J. M. et al. (2017) ‘Review of IoT applications in agro-industrial and environmental fields’, Computers and Electronics in Agriculture, 142(118), pp. 283–297. doi: 10.1016/j.compag.2017.09.015.
Tan, Y. K. and Panda, S. K. (2010) ‘Review of Energy Harvesting Technologies for Sustainable Wireless Sensor Network’, in Tan, Y. K. and Seah, W. (eds) Sustainable Wireless Sensor Networks. Rijeka, Croatia: InTech, pp. 15–43. doi: 10.5772/663.
Tanaka, K. (2018) ‘Low Delay Data Gathering Method for Rice Cultivation Management System IoT specialized outdoor communication procedure’, in 2018 International Conference on Information and Computer Technologies (ICICT). IEEE, pp. 139–143. doi: 10.1109/INFOCT.2018.8356857.
Temprilho, A. et al. (2018) ‘M2M Communication Stack for Intelligent Farming’, in Global Internet of Things Summit (GIoTS). doi: 10.1109/GIOTS.2018.8534560.
Terman, G. L. et al. (1969) ‘Yield-Protein Relationships in Wheat Grain, as Affected by Nitrogen and Water’, Agronomy Journal, 61(5), pp. 755–759. doi: 10.2134/agronj1969.00021962006100050031x.
Tieppo, R. C. et al. (2019) ‘Modeling cost and energy demand in agricultural machinery fl eets for soybean and maize cultivated using a no-tillage system’, Computers and Electronics in Agriculture. Elsevier, 156(November 2018), pp. 282–292. doi: 10.1016/j.compag.2018.11.032.
Tilman, D. et al. (2002) ‘Agricultural sustainability and intensive production practices.’, Nature, 418(6898), pp. 671–7. doi: 10.1038/nature01014.
Tilman, D. et al. (2011) ‘Global food demand and the sustainable intensification of agriculture’, in Proceedings of the National Academy of Sciences of the United States of America, pp. 20260–20264. doi: 10.1073/pnas.1116437108.
Tim Chamen, W. C. et al. (2015) ‘Mitigating arable soil compaction: A review and analysis of available cost and benefit data’, Soil and Tillage Research, 146, pp. 10–25. doi: https://doi.org/10.1016/j.still.2014.09.011.
Toth, P. and Vigo, D. (2002) The Vehicle Routing Problem. Edited by P. Toth and D. Vigo. Bologna, Italy: SIAM publications. doi: 10.1137/1.9780898718515.
Tozer, P. R. and Isbister, B. J. (2007) ‘Is it economically feasible to harvest by management zone ?’, Precision Agriculture, 8, pp. 151–159. doi: 10.1007/s11119-007-9035-z.
Tuna, G. et al. (2017) ‘A survey on information security threats and solutions for Machine to Machine (M2M) communications’, Journal of Parallel and Distributed Computing. Elsevier Inc., 109, pp. 142–154. doi: 10.1016/j.jpdc.2017.05.021.
Tuna, G. and Gungor, V. C. (2016) Energy harvesting and battery technologies for powering wireless sensor networks, Industrial Wireless Sensor Networks. Elsevier Ltd. doi: 10.1016/B978-1-78242-230-3.00002-7.
Tzounis, A. et al. (2017) ‘Internet of Things in agriculture, recent advances and future challenges’, Biosystems Engineering. Elsevier Ltd, 164, pp. 31–48. doi: 10.1016/j.biosystemseng.2017.09.007.
Uddin, M. A. et al. (2018) ‘UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring’, Sensors, 18(2), p. 555. doi: 10.3390/s18020555.
Utamima, A., Reiners, T. and Ansaripoor, A. H. (2019) ‘Optimisation of agricultural routing planning in field logistics with Evolutionary Hybrid Neighbourhood Search’, Biosystems Engineering. Elsevier Ltd, 184, pp. 166–180. doi: 10.1016/j.biosystemseng.2019.06.001.
VA (2020) Høstinformation 2020. Herning, Denmark. Available at: https://www.vja.dk/media/zkvgtbat/høstinfo_2020_web.pdf.
Veer, H. van der and Wiles, A. (2008) Achieving Technical Interoperability - the ETSI Approach, ETSI White Paper. Available at: https://www.etsi.org/images/files/ETSIWhitePapers/IOP whitepaper Edition 3 final.pdf.
Vellema, S., Struik, P. C. and Slingerland, M. (2020) ‘The society and the journal : Making interdisciplinarity a special issue in the life sciences’, NJAS - Wageningen Journal of Life Sciences. Elsevier B.V., 92(December), p. 100341. doi: 10.1016/j.njas.2020.100341.
Vellidis, G. et al. (2008) ‘A real-time wireless smart sensor array for scheduling irrigation’, Computers and Electronics in Agriculture, 61(1), pp. 44–50. doi: 10.1016/j.compag.2007.05.009.
Verdouw, C. (2016a) ‘Internet of Things in agriculture.’, CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 11(035). doi: 10.1079/PAVSNNR201611035.
Verdouw, C. (2016b) ‘Internet of Things in agriculture.’, CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 11(035).
Verdouw, C. et al. (2017) ‘IOF2020 : Fostering business and software ecosystems for large-scale uptake of IoT in food and farming’, in The International Tri-Conference for Precision Agriculture in 2017. doi: 10.5281/zenodo.1002903.
Vermeulen, G. D. and Mosquera, J. (2009) ‘Soil , crop and emission responses to seasonal-controlled traffic in organic vegetable farming on loam soil’, Soil & Tillage Research, 102, pp. 126–134. doi: 10.1016/j.still.2008.08.008.
Viljanen, N. et al. (2018) ‘A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model , Images and Vegetation Indices Captured by a Drone’, Agriculture, 8(5)(70), pp. 1–28. doi: 10.3390/agriculture8050070.
Villa-Henriksen, A. et al. (2018) ‘Internet-Based Harvest Fleet Logistic Optimisation’, in Groot Koerkamp, P. W. G. et al. (eds) Proceedings of the European Agricultural Conference, 8-12 July, Wageningen, the Netherlands. Wageningen, The Netherlands: Wageningen University and Research, pp. 56–61. doi: 10.18174/471679.
Villa-Henriksen, A., Skou-Nielsen, N., et al. (2020) ‘Infield optimized route planning in harvesting operations for risk of soil compaction reduction’, Soil Use and Management, 2020(May), pp. 1–12. doi: 10.1111/sum.12654.
Villa-Henriksen, A., Edwards, G. T. C., et al. (2020) ‘Internet of Things in arable farming: Implementation , applications , challenges and potential’, Biosystems Engineering, 191, pp. 60–84. doi: 10.1016/j.biosystemseng.2019.12.013.
Vuran, M. C. et al. (2018) ‘Internet of underground things in precision agriculture : Architecture and technology aspects’, Ad Hoc Networks. Elsevier B.V., 81, pp. 160–173. doi: 10.1016/j.adhoc.2018.07.017.
Wang, H. Z. et al. (2014) ‘Management of Big Data in the Internet of Things in Agriculture Based on Cloud Computing’, Applied Mechanics and Materials, 548–549, pp. 1438–1444. doi: 10.4028/www.scientific.net/AMM.548-549.1438.
Whetton, R. L., Waine, T. W. and Mouazen, A. M. (2018) ‘Evaluating management zone maps for variable rate fungicide application and selective harvest’, Computer and Electronic in Agriculture, 153(October), pp. 202–212. doi: 10.1016/j.compag.2018.08.004.
Wojciechowski, T. et al. (2016) ‘Rapeseed Seeds Quality Classification with Usage of VIS-NIR Fiber Optic Probe and Artificial Neural Networks’, in 2016 International Conference on Optoelectronics and Image Processing, pp. 44–48.
Wolanin, A. et al. (2019) ‘Remote Sensing of Environment Estimating crop primary productivity with Sentinel-2 and Landsat 8 using machine learning methods trained with radiative transfer simulations’, Remote Sensing of
Environment. Elsevier, 225(March), pp. 441–457. doi: 10.1016/j.rse.2019.03.002.
Wolfert, S. et al. (2017) ‘Big Data in Smart Farming – A review’, Agricultural Systems. The Authors, 153, pp. 69–80. doi: 10.1016/j.agsy.2017.01.023.
World Bank (2017) ICT in Agriculture: Connecting Smallholders to Knowledge, Networks, and Institutions. Updated Ed. Washington DC: The World Bank Group. doi: 10.1596/978-1-4648-1002-2.
Xangsayasane, P. et al. (2019) ‘Combine harvesting efficiency as affected by rice field size and other factors and its implication for adoption of combine contracting service’, Plant Production Science. Taylor & Francis, 22(1), pp. 68–76. doi: 10.1080/1343943X.2018.1561196.
Xian, K. (2017) ‘Internet of Things Online Monitoring System Based on Cloud Computing’, 13(9), pp. 123–131.
Yan, M. et al. (2018) ‘Field microclimate monitoring system based on wireless sensor network’, Journal of Intelligent & Fuzzy Systems, 35(2), pp. 1325–1337. doi: 10.3233/JIFS-169676.
Zhai, A. F. (2017) ‘Optimization of Agricultural Production Control Based on Data Processing Technology of Agricultural Internet of Things’, Italian Journal of Pure and Applied Mathematics, 38, pp. 243–252.
Zhang, A. et al. (2017) Accelerating precision agriculture to decision agriculture : the needs and drivers for the present and future of digital agriculture in Australia, A cross-industry producer survey for the Rural R&D for Profit ‘Precision to Decision’ (P2D) project.
Zhao, W. et al. (2018) ‘Design and Implementation of Smart Irrigation System Based on LoRa’, in 2017 IEEE Globecom Workshops (GC Wkshps). Singapore, Singapore: IEEE, pp. 1–6. doi: 10.1109/GLOCOMW.2017.8269115.
Zhao, X. et al. (2018) ‘Reliable IoT Storage : Minimizing Bandwidth Use in Storage Without Newcomer Nodes’, IEEE Communications Letters, 22(7), pp. 1462–1465. doi: 10.1109/LCOMM.2018.2831669.
Zhou, K. et al. (2014) ‘Agricultural operations planning in fields with multiple obstacle areas’, Computers and Electronics in Agriculture, 109, pp. 12–22. doi: 10.1016/j.compag.2014.08.013.
Zou, Y. and Quan, L. (2017) ‘A new service-oriented grid-based method for AIoT application and implementation’, Modern Physics Letters B, 31(19–21), p. 1740064. doi: 10.1142/S0217984917400644.
Zude-Sasse, M. et al. (2016) ‘Applications of precision agriculture in horticultural crops’, Eur. J. Hort. Sci., 81(2), pp. 78–90. doi: 10.17660/eJHS.2016/81.2.2.
