Enhancing Sustainable Crop Production through Innovations in Precision Agriculture Technologies

Ram Naresh

ICAR-ATARI, ZONE III, Kanpur, India.

N K Singh *

ICAR-ATARI-Krishi Vigyan Kendra, Pratapgarh, Uttar Pradesh- 229408, India.

Prashun Sachan

Department of Agronomy, CSAUA&T Kanpur, India.

Lalita Kumar Mohanty

KVK Jajpur Odisha, Odisha University of Agriculture and Technology, India.

Sweta Sahoo

Institute of Agricultural sciences, SOA University Bhubaneswar, India.

Shivam Kumar Pandey

Rashtriya Raksha University, India.

Barinderjit Singh

Department of Food Science and Technology, I.K. Gujral Punjab Technical University, Kapurthala, Punjab, 144601, India.

*Author to whom correspondence should be addressed.


Abstract

Precision agriculture technologies provide innovative tools to optimize crop production while minimizing environmental impacts. This review examines recent advances in precision ag systems to enhance sustainable agriculture. Key innovations include: remote and proximal crop sensing techniques leveraging hyperspectral imaging, thermal imaging, and Lidar to assess crop health and stress status; variable rate technologies like targeted sprayers and precision planters to reduce input waste; data analytics and decision support systems that integrate multi-source data streams to guide site-specific intervention; robotics and automation for precision field operations; and advanced breeding techniques and genomic tools enabling development of stress resilient, high yielding varieties. Adoption barriers, future technology trajectories, and priority research needs are discussed to further advance precision solutions that support productivity, efficiency, and sustainability goals.

Keywords: Precision agriculture, digital farming, site-specific crop management, remote sensing, crop phenotyping, variable rate application, agricultural robotics, decision support systems, genomic-assisted breeding, sustainable intensification


How to Cite

Naresh, R., Singh, N. K., Sachan, P., Mohanty, L. K., Sahoo, S., Pandey, S. K., & Singh, B. (2024). Enhancing Sustainable Crop Production through Innovations in Precision Agriculture Technologies. Journal of Scientific Research and Reports, 30(3), 89–113. https://doi.org/10.9734/jsrr/2024/v30i31861

Downloads

Download data is not yet available.

References

Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828-831.

Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems engineering, 114(4), 358-371.

Robertson, M. J., Llewellyn, R. S., Mandel, R., Lawes, R., Bramley, R. G., Swift, L., Metz, N., & O’Callaghan, C. (2012). Adoption of variable rate fertiliser application in the Australian grains industry: status, issues and prospects. Precision Agriculture, 13(2), 181-199.

Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture—a worldwide overview. Computers and electronics in agriculture, 36(2-3), 113-132.

Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828-831.

Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., ... & Balzer, C. (2011). Solutions for a cultivated planet. Nature, 478(7369), 337-342.

Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems engineering, 114(4), 358-371.

Walter, A., Finger, R., Huber, R., & Buchmann, N. (2017). Opinion: Smart farming is key to developing sustainable agriculture. Proceedings of the national academy of sciences, 114(24), 6148-6150.

Hunter, M. C., Smith, R. G., Schipanski, M. E., Atwood, L. W., & Mortensen, D. A. (2017). Agriculture in 2050: Recalibrating targets for sustainable intensification. BioScience, 67(4), 386-391.

Springmann, M., Clark, M., Mason-D'Croz, D., Wiebe, K., Bodirsky, B. L., Lassaletta, L., ... & Jonell, M. (2018). Options for keeping the food system within environmental limits. Nature, 562(7728), 519-525.

Smith, P., Nkem, J., Calvin, K., Campbell, D., Cherubini, F., Grassi, G., ... & Taboada, M. A. (2020). Which practices co-deliver food security, climate change mitigation and adaptation, and combat land degradation and desertification?. Global change biology, 26(3), 1532-1575.

Tilman, D., Balzer, C., Hill, J., & Befort, B. L. (2011). Global food demand and the sustainable intensification of agriculture. Proceedings of the national academy of sciences, 108(50), 20260-20264.

Muller, A., Schader, C., El-Hage, A. S., Gattinger, A., Seufert, V., Munafo, J., ... & Garnett, T. (2017). Strategies for feeding the world more sustainably with organic agriculture. Nature communications, 8(1), 1-13.

Lesk, C., Rowhani, P., & Ramankutty, N. (2016). Influence of extreme weather disasters on global crop production. Nature, 529(7584), 84-87.

Hunter, M. C., Smith, R. G., Schipanski, M. E., Atwood, L. W., & Mortensen, D. A. (2017). Agriculture in 2050: Recalibrating targets for sustainable intensification. BioScience, 67(4), 386-391.

Walter, A., Finger, R., Huber, R., & Buchmann, N. (2017). Opinion: Smart farming is key to developing sustainable agriculture. Proceedings of the national academy of sciences, 114(24), 6148-6150.

Springmann, M., Clark, M., Mason-D'Croz, D., Wiebe, K., Bodirsky, B. L., Lassaletta, L., ... & Jonell, M. (2018). Options for keeping the food system within environmental limits. Nature, 562(7728), 519-525.

Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828-831.

Walter, A., Finger, R., Huber, R., & Buchmann, N. (2017). Opinion: Smart farming is key to developing sustainable agriculture. Proceedings of the national academy of sciences, 114(24), 6148-6150.

Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems engineering, 114(4), 358-371.

Walter, A., Finger, R., Huber, R., & Buchmann, N. (2017). Opinion: Smart farming is key to developing sustainable agriculture. Proceedings of the national academy of sciences, 114(24), 6148-6150.

Pinter, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S., & Upchurch, D. R. (2003). Remote sensing for crop management. Photogrammetric engineering & remote sensing, 69(6), 647-664.

Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems engineering, 114(4), 358-371.

Matese, A., Toscano, P., Di Gennaro, S. F., Genesio, L., Vaccari, F. P., Primicerio, J., ... & Gioli, B. (2015). Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote sensing, 7(3), 2971-2990.

Fiorani, F., & Schurr, U. (2013). Future scenarios for plant phenotyping. Annual review of plant biology, 64, 267-291.

Araus, J. L., & Cairns, J. E. (2014). Field high-throughput phenotyping: the new crop breeding frontier. Trends in plant science, 19(1), 52-61.

Huber, S., Kneubühler, M., Psomas, A., Itten, K., & Zimmermann, N. E. (2018). Estimating foliar biochemistry from hyperspectral data in mixed forest canopy. Forest ecology and management, 421, 159-170.

Li, W., Niu, Z., Chen, H., Li, D., Wu, M., & Zhao, W. (2016). Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecological indicators, 67, 637-648.

Matese, A., Toscano, P., Di Gennaro, S. F., Genesio, L., Vaccari, F. P., Primicerio, J., ... & Gioli, B. (2015). Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote sensing, 7(3), 2971-2990.

Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldύ, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37.

Adamchuk, Viacheslav I., J. W. Hummel, M. T. Morgan, and S. K. Upadhyaya. "On-the-go soil sensors for precision agriculture." Computers and electronics in agriculture 44, no. 1 (2004): 71-91.

Samborski, S. M., Tremblay, N., & Fallon, E. (2009). Strategies to make use of plant sensors-based diagnostic information for nitrogen recommendations. Agronomy Journal, 101(4), 800-816.

Tremblay, N., Wang, Z., & Ma, B. L. (2012). Fluorescence-based sensors for the early detection of crop nutrient and environmental stresses. In Fluorescence sensing technology: Environmental and agricultural applications (pp. 71-93). ACS Publications.

Adamchuk, Viacheslav I., R. E. Lund, B. A. Reed, and D. W. Sudduth. "Soil electrical conductivity mapping." In Precision agriculture, pp. 739-774. American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 2005.

Adams, R., Bramley, R., Handmer, J., Smith, T. F., & Watson, I. (2007). Improving flood warnings in Europe: a research and policy agenda. Environmental Hazards, 7(1), 19-28.

Krishna, K. R. (2013). Agro-meteorological aspects of agriculture: Factors and effects. Agric Eng Int CIGR J, 15(3), 11-26.

Robertson, M. J., Llewellyn, R. S., Mandel, R., Lawes, R., Bramley, R. G., Swift, L., ... & O’Callaghan, C. (2012). Adoption of variable rate fertiliser application in the Australian grains industry: status, issues and prospects. Precision Agriculture, 13(2), 181-199.

Pierce, F. J., & Nowak, P. (1999). Aspects of precision agriculture. In Advances in agronomy (Vol. 67, pp. 1-85). Academic Press.

Smith, R. J., Raine, S. R., & Minkevich, J. (2005). Irrigation application efficiency and deep drainage potential under surface irrigated cotton. Agricultural water management, 71(2), 117-130.

Liu, W., Tollenaar, M., Stewart, G., & Deen, W. (2004). Response of corn grain yield to spatial and temporal variability in emergence. Crop science, 44(3), 847-854.

Gonçalves, J. C., & Silva, C. (2016). Water profiling in precision irrigation with wireless sensor networks. IEEE Instrumentation & Measurement Magazine, 19(4), 37-43.

Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147, 70-90.

Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of things in agriculture, recent advances and future challenges. Biosystems engineering, 164, 31-48.

Vuran, M. C., Salam, A., Wong, R., & Irmak, S. (2018). Internet of underground things in precision agriculture: Architecture and technology aspects. Ad Hoc Networks, 81, 160-173.

Fu, J., Liu, J., Li, Y., & Wu, M. (2020). A wheat lodging monitoring system based on RGB image feature and machine learning methods. Comput Electron Agric, 174, 105478.

Pérez-Ortiz, M., Peña, J. M., Gutiérrez, P. A., Hervás-Martínez, C., & López-Sánchez, J. (2015). A selectable threshold for Automatic Row Detection of vegetable crops using machine vision and metaheuristic optimization. Expert systems with applications, 42(4), 2170-2182.

Peteinatos, G. G., & Vrindts, E. (2021). Hyperspectral Remote Sensing for Precision Vegetable Seed Production. Remote Sensing, 13(15), 3094.

Honsdorf, N., March, T. J., Berger, B., Tester, M., & Pillen, K. (2014). High-throughput phenotyping to detect drought tolerance QTL in wild barley introgression lines. PloS one, 9(5), e97047.

Fu, J., Liu, J., Li, Y., & Wu, M. (2020). A wheat lodging monitoring system based on RGB image feature and machine learning methods. Comput Electron Agric, 174, 105478.

Peteinatos, G. G., & Vrindts, E. (2021). Hyperspectral Remote Sensing for Precision Vegetable Seed Production. Remote Sensing, 13(15), 3094.

Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big data in smart farming - A review. In Agricultural systems (Vol. 153, pp. 69-80). Elsevier BV.

Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147, 70-90.

Li, X., Li, X., Rothstein, A., Jennings, E., & Hequet, E. (2021). Opportunities and challenges in sustainable cotton production: a data driven review of input-use efficiency focusing on nutrients, water and energy. Environmental Research Letters, 16(2), 023001.

Aubert, B. A., Schroeder, A., & Grimaudo, J. (2012). IT as enabler of sustainable farming: An empirical analysis of farmers' adoption decision of precision agriculture technology. Decision support systems, 54(1), 510-520.

Cai, J., Zhang, C., Zhang, W., Liu, M., Grieve, B. D., & Lyon, J. (2021). Sensors and digital twins for smart agriculture. Nature Food, 2(2), 104-105.

Hu, G., Tay, W. P., & Wen, Y. (2018). Cloud robotics: architecture, challenges and applications. IEEE network, 26(3), 21-28.

Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828-831.

Robertson, M. J., Llewellyn, R. S., Mandel, R., Lawes, R., Bramley, R. G., Swift, L., ... & O’Callaghan, C. (2012). Adoption of variable rate fertiliser application in the Australian grains industry: status, issues and prospects. Precision Agriculture, 13(2), 181-199.

Franzen, D. W., Kitchen, N. R., Holland, K. H., Schepers, J. S., & Raun, W. R. (2016). Algorithms for in-season nutrient management in cereals. Agronomy Journal, 108(5), 1775-1781.

Reis, P. R., Oliveira, L. C., Silva, C. A., Leite, I. C., Alexandre, C., & Bredehoeft, C. (2015). Geospatial evaluation for ecological-economic zonation of honeybee pesticide exposure risk in an intensive agricultural landscape. Ecological indicators, 52, 151-160.

Morari, F., Castrignanò, A., & Pagliarin, C. (2009). Application of multivariate geostatistics in delineating management zones within a gravelly vineyard using geo-electrical sensors. Computers and electronics in agriculture, 68(1), 97-107.

Diacono, M., Rubino, P., & Montemurro, F. (2013). Precision nitrogen management of wheat. A review. In Agronomy for Sustainable Development (Vol. 33, No. 1, pp. 219-241). Springer-Verlag.

Robertson, G. P., Gross, K. L., Hamilton, S. K., Landis, D. A., Schmidt, T. M., Snapp, S. S., & Swinton, S. M. (2014). Farming for ecosystem services: An ecological approach to production agriculture. BioScience, 64(5), 404-415.

Plant, R. E. (2001). Site-specific management: the application of information technology to crop production. Computers and electronics in agriculture, 30(1-3), 9-29.

Liu, W., Tollenaar, M., Stewart, G., & Deen, W. (2004). Response of corn grain yield to spatial and temporal variability in emergence. Crop science, 44(3), 847-854.

Barker, D. W., & Sawyer, J. E. (2012). Using active canopy sensors to quantify variability in corn and soybean. American Journal of Plant Sciences, 3(10), 1397.

Pasley, H. R., Stevens, W. B., & Barker, D. W. (2021). Estimating the capacity for site-specific nitrogen fertilizer rates to increase corn yields. Agronomy Journal.

Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Mullen, R. W., Freeman, K. W., ... & Lukina, E. V. (2002). Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agronomy Journal, 94(4), 815-820.

Skaggs, R. W., Youssef, M. A., & Chescheir, G. M. (2012). DRAINMOD: Model use, calibration, and validation. Transactions of the ASABE, 55(4), 1509-1522.

Hedley, C. B., Roudier, P., Yule, I. J., Ekanayake, J., & Bradbury, S. (2013). Soil water status and water table depth modelling using electromagnetic surveys for precision irrigation scheduling. Geoderma, 199, 22-29.

Giles, D. K., & Slaughter, D. C. (1997). Precision band spraying with machine-vision guidance and adjustable yaw nozzles. Transactions of the ASAE, 40(1), 29-36.

Hedley, C. B., & Yule, I. J. (2009). A method for spatial prediction of daily soil water status for precise irrigation scheduling. Agricultural Water Management, 96(12), 1737-1745.

Haghverdi, A., Leib, B. G., Washington-Allen, R. A., Ayers, P. D., & Buschermohle, M. J. (2015). Perspectives on delineating management zones for variable rate irrigation. Computers and Electronics in Agriculture, 117, 154-167.

Peters, R. T., & Evett, S. R. (2008). Automation of a center pivot using the temperature-time-threshold method of irrigation scheduling. Journal of irrigation and drainage engineering, 134(3), 286-291.

Hedley, C. B., Roudier, P., Yule, I. J., Ekanayake, J., & Bradbury, S. (2013). Soil water status and water table depth modelling using electromagnetic surveys for precision irrigation scheduling. Geoderma, 199, 22-29.

Vuran, M. C., Salam, A., Wong, R., & Irmak, S. (2018). Internet of underground things in precision agriculture: Architecture and technology aspects. Ad Hoc Networks, 81, 160-173.

Smith, R. J., Raine, S. R., & Minkevich, J. (2005). Irrigation application efficiency and deep drainage potential under surface irrigated cotton. Agricultural water management, 71(2), 117-130.

Ayars, J. E., Phene, C. J., Hutmacher, R. B., Davis, K. R., Schoneman, R. A., Vail, S. S., & Mead, R. M. (1999). Subsurface drip irrigation of row crops: a review of 15 years of research at the Water Management Research Laboratory. Agricultural water management, 42(1), 1-27.

Wanjogu, R. K., Koopmans, A., Owens, V. N., & LaBorde, L. F. (2019). Biological Risks and Market Impacts Associated with Irrigation Water Transfers. Sustainability, 11(1), 6.

Robertson, G. P., Gross, K. L., Hamilton, S. K., Landis, D. A., Schmidt, T. M., Snapp, S. S., & Swinton, S. M. (2014). Farming for ecosystem services: An ecological approach to production agriculture. BioScience, 64(5), 404-415.

Franzen, D. W., Sharma, L. K., Bu, H., & Daggupati, P. (2019). Field characterization of soil variability using on‐the‐go soil sensors: A case study in North Dakota, USA. Proceedings of the 11th Global Workshop on Proximal Soil Sensing, Columbia.

Adamchuk, Viacheslav I., J. W. Hummel, M. T. Morgan, and S. K. Upadhyaya. "On-the-go soil sensors for precision agriculture." Computers and electronics in agriculture 44, no. 1 (2004): 71-91.

Xie, M., Tremblay, N., Tremblay, G., Bourgault, M., Leblanc, M. C., & Deen, W. (2010). Development of a nitrogen management tool for corn production in eastern Canada. Agronomy Journal, 102(2), 464-473.

Chen, S., & Subler, S. (2007). Theoretical insights into the relationship between precision agriculture technology adoption and farm size. Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, 55(4), 457-470.

Mamo, M., Malzer, G. L., Mulla, D. J., Huggins, D. R., & Strock, J. (2003). Spatial and temporal variation in economically optimum nitrogen rate for corn. Agronomy journal, 95(4), 958-964.

Barker, D. W., & Sawyer, J. E. (2010). Using active canopy sensors to quantify corn nitrogen stress and nitrogen application rate. Agronomy journal, 102(3), 964-971.

Anand, M., Bahl, N., Kumar, A. A., Sarma, P., & Saxena, R. (2020). Crop and nutrient losses from uniform and variable rate fertiliser application. Biosystems Engineering, 194, 100-118.

Giles, D. K., & Slaughter, D. C. (1997). Precision band spraying with machine-vision guidance and adjustable yaw nozzles. Transactions of the ASAE, 40(1), 29-36.

Reis, P. R., Oliveira, L. C., Silva, C. A., Leite, I. C., Alexandre, C., & Bredehoeft, C. (2015). Geospatial evaluation for ecological-economic zonation of honeybee pesticide exposure risk in an intensive agricultural landscape. Ecological indicators, 52, 151-160.

Tangwongkit, R., Salokhe, V. M., & Jayasuriya, H. P. (2006). Development of a real-time variable rate sprayer for tree crops. Agricultural Engineering International: CIGR Journal. 8, FP 05 013.

Giles, D. K., Delwiche, M. J., & Dodd, R. B. (1989). Sprayer control by sensing orchard crop characteristics: Orchard architecture and spray liquid savings. Journal of agricultural Engineering research, 43, 271-289.

Midtiby, H. S., Åstrand, B., Jørgensen, O., & Jørgensen, R. N. (2011). Upper limit for context-based crop classification in robotic weeding applications. Biosystems Engineering, 110(2), 157-167.

Diacono, M., Rubino, P., & Montemurro, F. (2013). Precision nitrogen management of wheat. In Agronomy for Sustainable Development (Vol. 33, No. 1, pp. 219-241). Springer-Verlag.

Roberts, D. C., Brorsen, B. W., Taylor, R. K., Solie, J. B., & Raun, W. R. (2012). Replicability of nitrogen recommendations from ramped calibration strips in winter wheat. Agronomy journal, 104(1), 26-33.

Schnitkey, G., Hopkins, J., & Tweeten, L. (1996). An economic evaluation of precision fertilizer applications on corn-soybean fields. Precision agriculture, 2, 977-987.

Koch, B., Khosla, R., Frasier, W. M., Westfall, D. G., & Inman, D. (2004). Economic feasibility of variable-rate nitrogen application utilizing site-specific management zones. Agronomy Journal, 96(6), 1572-1580.

Basso, B., Fiorentino, C., Cammarano, D., Cafiero, G., & Dardanelli, J. (2012). Analysis of rainfall distribution on spatial and temporal patterns of wheat yield in Mediterranean environment. European Journal of Agronomy, 41, 52-65.

Munack, A., Reckling, M., Dabbert, S., Gaudchau, E., Schule, G., Zimmer, J., & Sieling, K. (2014). Modeling standards to improve fertilizer recommendations towards cropping system goals–review of current approaches. Crop pasture science.

Plauborg, F., Iversen, B. V., Lægdsmand, M., Bergman, P., & Andersen, M. N. (2016). Precision agriculture and the maze of digital (bio-) physical interrelations optimizing growth and production. NJAS-Wageningen Journal of Life Sciences, 78, 103-108.

Brown, P. D., Cochrane, T. A., & Krom, T. D. (2010). Optical sensing of pasture nutrients: the potential for proximal sensing of potassium in grazed pastures. International journal of remote sensing, 31(17-18), 4503-4522.

Teal, R. K., Tubana, B., Girma, K., Freeman, K. W., Arnall, D. B., Walsh, O., & Raun, W. R. (2006). In-season prediction of corn grain yield potential using normalized difference vegetation index. Agronomy journal, 98(6), 1488-1494.

Hedley, C. B., & Yule, I. J. (2009). Soil water status mapping and two variable-rate irrigation scenarios. Precision Agriculture, 10(4), 342-355.

Schepers, A. R., Shanahan, J. F., Liebig, M. A., Schepers, J. S., Johnson, S. H., & Luchiari, A. (2004). Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agronomy Journal, 96(1), 195-203.

Hornung, A., Khosla, R., Reich, R., Inman, D., & Westfall, D. G. (2006). Comparison of site‐specific management zones: soil color based and yield based. Agronomy journal, 98(2), 405-417.

Li, Y., Shi, Z., Li, F., & Li, H. Y. (2007). Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land. Computers and Electronics in Agriculture, 56(2), 174-186.

Guastaferro, F., Castrignanò, A., De Benedetto, D., Sollitto, D., Troccoli, A., & Cafarelli, B. (2010). A comparison of different algorithms for the delineation of management zones. Precision Agriculture, 11(6), 600-620.

Schepers, A. R., Shanahan, J. F., Liebig, M. A., Schepers, J. S., Johnson, S. H., & Luchiari, A. (2004). Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agronomy Journal, 96(1), 195-203.

Diker, K., & Bausch, W. C. (2003). Potential use of electrical conductivity for delineating management zones. Precision Agriculture, 4(4), 389-408.

Adamchuk, Viacheslav I., J. W. Hummel, M. T. Morgan, and S. K. Upadhyaya. "On-the-go soil sensors for precision agriculture." Computers and electronics in agriculture 44, no. 1 (2004): 71-91.

Schepers, A. R., Shanahan, J. F., Liebig, M. A., Schepers, J. S., Johnson, S. H., & Luchiari, A. (2004). Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agronomy Journal, 96(1), 195-203.

Bramley, R. (2009). Lessons from nearly 20 years of precision agriculture research, development, and adoption as a guide to its appropriate application. Crop and Pasture Science, 60(3), 197-217.

Hedley, C. B., & Yule, I. J. (2009). Soil water status mapping and two variable-rate irrigation scenarios. Precision Agriculture, 10(4), 342-355.

Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Mullen, R. W., Freeman, K. W., ... & Lukina, E. V. (2002). Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agronomy Journal, 94(4), 815-820.

Barbedo, J. G. A. (2016). Factors influencing the use of deep learning for plant disease recognition. Biosystems engineering, 142, 15-22.

Robertson, M. J., Llewellyn, R. S., Mandel, R., Lawes, R., Bramley, R. G., Swift, L., ... & O’Callaghan, C. (2012). Adoption of variable rate fertiliser application in the Australian grains industry: status, issues and prospects. Precision Agriculture, 13(2), 181-199.

Dong, X., Vuran, M. C., & Irmak, S. (2013). Autonomous precision agriculture through integration of wireless underground sensor networks with center pivot irrigation systems. Ad Hoc Networks, 11(7), 1975-1987.

Basso, B., Fiorentino, C., Cammarano, D., Cafiero, G., & Dardanelli, J. (2012). Analysis of rainfall distribution on spatial and temporal patterns of wheat yield in Mediterranean environment. European Journal of Agronomy, 41, 52-65.

Camino, C., González-Dugo, V., Hernández, P., & Zarco-Tejada, P. J. (2022). Radiometrics-Derived High-Resolution Crop Coefficients and Heat Stress Crop Maps Using Multispectral and Thermal UAV Imagery. Remote Sensing, 14(3), 759.

Aubert, B. A., Schroeder, A., & Grimaudo, J. (2012). IT as enabler of sustainable farming: An empirical analysis of farmers' adoption decision of precision agriculture technology. Decision support systems, 54(1), 510-520.

Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828-831.

Ju, X. T., Xing, G. X., Chen, X. P., Zhang, S. L., Zhang, L. J., Liu, X. J., ... & Christie, P. (2009). Reducing environmental risk by improving N management in intensive Chinese agricultural systems. Proceedings of the national academy of sciences, 106(9), 3041-3046.

Helmers, M. J., Zhou, X., Asbjornsen, H., Kolka, R., Tomer, M. D., & Cruse, R. M. (2012). Sediment removal by prairie filter strips in row-cropped ephemeral watersheds. Journal of environmental quality, 41(5), 1531-1539.

Anand, M., Bahl, N., Kumar, A. A., Sarma, P., & Saxena, R. (2020). Crop and nutrient losses from uniform and variable rate fertiliser application. Biosystems Engineering, 194, 100-118.

Miller, P., Lanier, W., & Brandt, S. (2018). Using growing degree days to predict plant stages. Montana State University, USA, 1-12.

Franzen, D. W. (2017). Nutrient management planning. Precision agriculture basics, 57-68.

Li, H., Liang, W. L., Zhang, X. Z., Bao, X. G., & Zhang, H. L. (2007). Nitrogen placement and source effects on nitrogen use efficiency and plant growth in newly planted apple orchards. Scientia Horticulturae, 112(2), 173-181.

Cameron, K. C., Di, H. J., & Moir, J. L. (2013). Nitrogen losses from the soil/plant system: a review. Annals of applied biology, 162(2), 145-173.

Basso, B., & Ritchie, J. T. (2015). Simulating crop growth and biogeochemical fluxes in response to land management using the SALUS model. In The ecology of agricultural landscapes: Long-term research on the path to sustainability (pp. 252-274). Oxford University Press.

Palm, C., Blanco-Canqui, H., DeClerck, F., Gatere, L., & Grace, P. (2014). Conservation agriculture and ecosystem services: An overview. Agriculture, Ecosystems & Environment, 187, 87-105.

Jha, K., & Muhammed, A. (2021). Applications of Remote Sensors in Precision Agriculture: A Systematic Review. Remote Sensing, 13(18), 3809.

Chamen, T., Moxey, A., Towers, W., Balana, B., & Hallett, P. (2015). Mitigating arable soil compaction: A review and analysis of available cost and benefit data. Soil and tillage research, 146, 10-25.

Antille, D. L., Bennett, J. M., & Jensen, T. A. (2016). Soil compaction and controlled traffic considerations in Australian cotton-farming systems. Crop and Pasture Science, 67(1), 1-28.

Giles, D. K., Delwiche, M. J., & Dodd, R. B. (1989). Sprayer control by sensing orchard crop characteristics: Orchard architecture and spray liquid savings. Journal of agricultural Engineering research, 43, 271-289.

Antille, D. L., Peets, S., Galambošová, J., Botta, G. F., Rataj, V., Macák, M., ... & Godwin, R. J. (2019). Review: Soil compaction and controlled traffic farming in arable and grass cropping systems. Agronomy Research, 17(3), 653-682.

Jha, K., & Muhammed, A. (2021). Applications of Remote Sensors in Precision Agriculture: A Systematic Review. Remote Sensing, 13(18), 3809.

Cerutti, A. K., Beccaro, G. L., Bruun, S., Bosco, S., Donno, D., Notarnicola, B., & Bounous, G. (2014). Life cycle assessment application in the fruit sector: State of the art and recommendations for environmental declarations of fruit products. Journal of cleaner production, 73, 125-135.

Ward, S. M. (2013). Costs and returns from precision agricultural practices. Journal of the ASFMRA.

Chen, S. Y., & Zhang, Y. (2019). Effects of agricultural management on soil carbon and nitrogen in the North China Plain: a comprehensive dataset derived from published literatures. Earth System Science Data, 11(2), 461-469.

Lioutas, E. D., & Charatsari, C. (2020). Smart farming and short food supply chains: Are they compatible?. Agriculture, 10(12), 647.

Grassini, P., Thorburn, J., Burr, C., & Cassman, K. G. (2011). High-yield irrigated maize in the Western US Corn Belt: II. Irrigation management and crop water productivity. Field Crops Research, 120(1), 133-141.

Basso, B., & Antle, J. (2020). Digital agriculture to design sustainable agricultural systems. Nature Sustainability, 3(4), 254-256.

Araus, J. L., Kefauver, S. C., Zaman-Allah, M., Olsen, M. S., & Cairns, J. E. (2018). Translating high-throughput phenotyping into genetic gain. Trends in plant science, 23(5), 451-466.

Rahaman, M. M., Chen, D., Gillani, Z., Klukas, C., & Chen, M. (2015). Advanced phenotyping and phenotype data analysis for the study of plant growth and development. Frontiers in plant science, 6, 619.

Spindel, J., & McCouch, S. (2016). When more is better: how data sharing would accelerate genomic selection of crop plants. New Phytologist, 212(4), 814-826.

Yang, W., Duan, L., Chen, G., Xiong, L., & Liu, Q. (2013). Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies. Curr Opin Plant Biol, 16(2), 180-187.

Cabrera-Bosquet, L., Crossa, J., von Zitzewitz, J., Serret, M. D., & Araus, J. L. (2012). High-throughput phenotyping and genomic selection: the frontiers of crop breeding converge. Journal of integrative plant biology, 54(5), 312-320.

Zia, S., Romano, G., Spreer, W., Sanchez, C., Cairns, J., Araus, J. L., & Müller, J. (2013). Infrared thermal imaging as a rapid tool for identifying water-stress tolerant maize genotypes of different phenology. Journal of Agronomy and Crop Science, 199(2), 75-84.

Marti, J., Bort, J., Slafer, G. A., & Araus, J. L. (2007). Can wheat yield be assessed by early measurements of Normalized Difference Vegetation Index?. Annals of applied biology, 150(3), 253 -257.

Fiorani, F., Rascher, U., Jahnke, S., & Schurr, U. (2012). Imaging plants dynamics in heterogenic environments. Current opinion in biotechnology, 23(2), 227-235.

Prashar, A., & Jones, H. G. (2014). Infra-red thermography as a high-throughput tool for field phenotyping. Agronomy, 4(3), 397-417.

Toju, H., Peay, K. G., Yamamichi, M., Narisawa, K., Hiruma, K., Naito, K., ... & Yoshida, K. (2018). Core microbiomes for sustainable agroecosystems. Nature plants, 4(5), 247-257.

Edwards, J., Johnson, C., Santos-Medellín, C., Lurie, E., Podishetty, N. K., Bhatnagar, S., ... & Sundaresan, V. (2015). Structure, variation, and assembly of the root-associated microbiomes of rice. Proceedings of the National Academy of Sciences, 112(8), E911-E920.

Thompson, J. N., & Burdon, J. J. (1992). Gene-for-gene coevolution between plants and parasites. Nature, 360(6400), 121-125.

Pandey, P., Irulappan, V., Bagavathiannan, M. V., & Senthil-Kumar, M. (2017). Impact of combined abiotic and biotic stresses on plant growth and avenues for crop improvement by exploiting physio-morphological traits. Frontiers in plant science, 8, 537.

Technow, F., Messina, C. D., Totir, L. R., & Cooper, M. (2015). Integrating crop growth models with whole genome prediction through approximate Bayesian computation. PloS one, 10(6), e0130855.

Chenu, K., Deihimfard, R., & Chapman, S. C. (2013). Large-scale characterization of drought pattern: a continent-wide modelling approach applied to the Australian wheatbelt–spatial and temporal trends. New Phytologist, 198(3), 801-820.

Tardieu, F., & Tuberosa, R. (2010). Dissection and modelling of abiotic stress tolerance in plants. Current opinion in plant biology, 13(2), 206-212.

Ghosh, S., Watson, A., Gonzalez-Navarro, O. E., Ramirez-Gonzalez, R. H., Yanes, L., Mendoza-Suárez, M., ... & Hickey, L. T. (2018). Speed breeding in growth chambers and glasshouses for crop breeding and model plant research. Nature protocols, 13(12), 2944-2963.

Watson, A., Ghosh, S., Williams, M. J., Cuddy, W. S., Simmonds, J., Rey, M. D., ... & Hickey, L. T. (2018). Speed breeding is a powerful tool to accelerate crop research and breeding. Nature plants, 4(1), 23-29.

Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture - a worldwide overview. Computers and Electronics in Agriculture, 36(2), 113-132.

Dillon, C. (2018). Precision agriculture technology adoption in New Zealand: Strategies for improving implementation and practice. PhD Thesis, Massey University.

Anand, M., Bahl, N., Kumar, A. A., Sarma, P., & Saxena, R. (2020). Crop and nutrient losses from uniform and variable rate fertiliser application. Biosystems Engineering, 194, 100-118.

Liu, L., Zhang, X., Zhang, Q., Jiang, B., Buchenauer, H., Han, Q., ... & Li, C. (2018). An efficient and anti‐leaching controlled release urea fertiliser coating prepared by polyurethane emulsion for reducing environmental pollution. Journal of the Science of Food and Agriculture, 98(3), 1197-1204.

Hedley, C. B., & Yule, I. J. (2009). Soil water status mapping and two variable-rate irrigation scenarios. Precision Agriculture, 10(4), 342-355.

Franzen, D. W., Kitchen, N. R., Holland, K. H., Schepers, J. S., & Raun, W. R. (2016). Algorithms for in-season nutrient management in cereals. Agronomy Journal, 108(5), 1775-1781.

Lowenberg-DeBoer, J. M., & Erickson, B. (2019). Setting the record straight on precision agriculture adoption. Agronomy Journal, 111(4), 1552-1569.

Schimmelpfennig, D. (2016). Farm profits and adoption of precision agriculture (No. 1477-2016-120935).

Pedersen, S. M., Fountas, S., & Blackmore, S. (2005). Economic potential of robots for high value crops and landscape treatment. Precision Agriculture, 6(5), 463-477.

Qin, W., Qiu, B., Xue, Y., Chen, C., Zuo, L., & Zhao, S. (2021). Progress, challenges and prospects of agricultural intelligent equipment. Engineering, 7(2), 175-185.

Kitchen, N. R., Snyder, C. J., Franzen, D. W., & Wiebold, W. J. (2002). Educational needs of precision agriculture. Precision Agriculture, 3(4), 341-351.

Fountas, S., Carli, G., Sørensen, C. G., Tsiropoulos, Z., Cavalaris, C., Vatsanidou, A., ... & Canavari, M. (2015). Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture, 115, 40-50.

Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision agriculture, 13(6), 693-712.

Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of things in agriculture, recent advances and future challenges. Biosystems engineering, 164, 31-48.

Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural Systems, 153, 69-80.

Matese, A., Baraldi, R., Berton, A., Cesaraccio, C., Di Gennaro, S. F., Duce, P., ... & Zaldei, A. (2018). Estimation of water stress in grapevines using proximal and remote sensing methods. Remote sensing, 10(1), 121.

Lamb, D. W., Frazier, P., & Adams, P. (2008). Improving pathways to adoption: Putting the right P's in precision agriculture. Computers and Electronics in Agriculture, 61(1), 4-9.

Daberkow, S. G., & McBride, W. D. (2003). Farm and operator characteristics affecting the awareness and adoption of precision agriculture technologies in the US. Precision agriculture, 4(2), 163-177.

Fountas, S., Pedersen, S. M., & Blackmore, S. (2005). ICT in precision agriculture–diffusion of technology. Diffusion of IT in the Danish Agricultural Sector. Department of Agricultural Engineering, Denmark, 1-12.

Ker, A., Toit, D., & Dass, T. (2003). Implementation of in-field rainwater harvesting for corn production under semi-arid agriculture conditions. Journal of Agronomy, 3(4), 219-227.

Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828-831.

Rotz, S., Duncan, E., Small, M., Botschner, J., Dara, R., Mosby, I., ... & Mutch, D. (2019). The Politics of Digital Agricultural Technologies: A Preliminary Review. Sociologia Ruralis, 59(2), 203-229.

Bechar, A., & Vigneault, C. (2016). Agricultural robots for field operations: Concepts and components. Biosystems Engineering, 149, 94-111.

Fennimore, S. A., Slaughter, D. C., Siemens, M. C., Leon, R. G., & Saber, M. N. (2016). Technology for automation of weed control in specialty crops. Weed Technology, 30(3), 823-837.

Walter, A., Finger, R., Huber, R., & Buchmann, N. (2017). Opinion: Smart farming is key to developing sustainable agriculture. Proceedings of the national academy of sciences, 114(24), 6148-6150.

Bronson, K., & Knezevic, I. (2016). Big Data in food and agriculture. Big data & society, 3(1), 2053951716648174.

Rotz, S., Duncan, E., Small, M., Botschner, J., Dara, R., Mosby, I., ... & Mutch, D. (2019). The Politics of Digital Agricultural Technologies: A Preliminary Review. Sociologia Ruralis, 59(2), 203-229.

Eastwood, C., Ayre, M., Nettle, R., & Dela Rue, B. (2019). Making sense in the cloud: farm advisory services in a smart farming future. NJAS-Wageningen Journal of Life Sciences, 90, 100291.

Erickson, B., Lowenberg-DeBoer, J., & Bradford, J. (2017). 2017 Precision agriculture dealership survey: Findings and insights.

Gozzer, S. (2021). Next generation agriculture: policy imperatives for transforming farming futures. Nature Food, 2(6), 397-402.

Rotz, S., Gravely, E., Mosby, I., Duncan, E., Finnis, E., Horgan, M., ... & Pant, L. (2019). Automated pastures and the digital divide: How agricultural technologies are shaping labour and rural communities. Journal of Rural Studies, 68, 112-122.

Kernecker, M., Knierim, A., & Wurbs, A. (2020). Experience counts! The role of farm experience for adopting precision farming technologies. NJAS-Wageningen Journal of Life Sciences, 92, 100340.

Kovács, I., & Husti, I. (2006). Technical requirements of precision agriculture and their appearance in agricultural education. US-China Education Review, 3(9), 49-57.

Rotz, S. (2018). Drawing lines in the corn rows: Making sense of boundaries and belonging between farmers and consultants in US corn belt agriculture. Agriculture and Human Values, 1-15.

Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of things in agriculture, recent advances and future challenges. Biosystems engineering, 164, 31-48.

Kernecker, M., Knierim, A., & Wurbs, A. (2020). Experience counts! The role of farm experience for adopting precision farming technologies. NJAS-Wageningen Journal of Life Sciences, 92, 100340

Kernecker, M., Knierim, A., & Wurbs, A. (2020). Experience counts! The role of farm experience for adopting precision farming technologies. NJAS-Wageningen Journal of Life Sciences, 92, 100340.

Tey, Y. S., & Brindal, M. (2012). Factors influencing the adoption of precision agricultural technologies: a review for policy implications. Precision Agriculture, 13(6), 713-730.

Lioutas, E. D., & Charatsari, C. (2020). Smart farming and short food supply chains: Are they compatible?. Agriculture, 10(12), 647.

Dillon, C. (2018). Precision agriculture technology adoption in New Zealand: Strategies for improving implementation and practice. PhD Thesis, Massey University.

Eastwood, C., Ayre, M., Nettle, R., & Dela Rue, B. (2019). Making sense in the cloud: farm advisory services in a smart farming future. NJAS-Wageningen Journal of Life Sciences, 90, 100291.

Fountas, S., Wulfsohn, D., Blackmore, B. S., Jacobsen, H. L., & Pedersen, S. M. (2006). A model of decision-making and information flows for information-intensive agriculture. Agricultural Systems, 87(2), 192-210.

Fountas, S., Carli, G., Sørensen, C. G., Tsiropoulos, Z., Cavalaris, C., Vatsanidou, A., ... & Canavari, M. (2015). Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture, 115, 40-50.

Vithayathil, J. (2018). Will big data help the farm or spoil the view?. International Journal of Rural Management, 14(1), 1-32.

Lioutas, E. D., & Charatsari, C. (2020). Smart farming and short food supply chains: Are they compatible?. Agriculture, 10(12), 647.

Kernecker, M., Knierim, A., & Wurbs, A. (2020). Experience counts! The role of farm experience for adopting precision farming technologies. NJAS-Wageningen Journal of Life Sciences, 92, 100340.

Bronson, K. (2019). Smart farming: Including rights holders for responsible agricultural innovation. Technology Innovation Management Review, 9(2).

Choudhary, V. P., Srivastava, P. K., Cai, X., Walia, M. K., Rahaman, J., & Variar, M. (2011). Biosaline agri-technology transfer through a functional farmer field school approach. Agricultural Water Management, 98(2), 267-276.

Dhaka, B. L., Meena, B. S., & Poonia, S. (2021). Digital agriculture: achievements and prospects. Int. J. Chem. Stud, 9(1), 1441-1447.

Malve, H. (2016). Precise farming at precision agriculture farm of ICAR-Indian Agricultural Research Institute, New Delhi. Current Agriculture Research Journal, 4(1).

Jeyarman, J., & Karthigai Reservation, R. M. (2018). Geo spatial technology for agriculture development. International Journal of Agriculture Sciences, 10(14), 7095-7097.

Gummagolmath, K. C., & Kumaraswamy, K. (2018, March). Internet of things based smart crop-field monitoring and automation irrigation system. In 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 2018 2nd International Conference on (pp. 612-617). IEEE.

Balasubramanian, A. (2017). Role of Geospatial Technology for Sustainable Agriculture Development in India. In Geospatial Technology-Environmental and Social Applications (pp. 59-72). IntechOpen.

Pazhanivelan, S., Nimish Kumar, B., Padmavathy, K., & Revathy, V. B. (2020). Skill development on precision farming technologies for enhancing employability of farm graduates. Journal of Pharmacognosy and Phytochemistry, 9(4).

Parihar, C. M., Khanpara, V. D., Singh, A. K., Vaja, Y. K., & Ghosh, N. (2019). Precision agriculture technologies adoption by the farmers in central plain zone of North Gujarat. Agric. Sci. Digest, 39(3), 175-181.

Trehan, A., Kaur, N., Kaur, S., & Kaur, T. (2017). Role and present status of precision agriculture in India. Int. J. Pure Appl. Biosci, 5(6), 1205-1210.

Jha, A. K., & Mall, A. K. (2014). Integrated weed management practices for enhancing crop yields in India: A review. Indian Journal of Agronomy, 59(1).

Singh, S., Singh, J., & Sharma, B. M. (2021). Digital agriculture: Mobile apps facilitating precision agriculture technologies adoption in India. Public Library of Science one, 16(10), e0259912.

Msibi, Z. L., & Oladele, O. I. (2021). Comparative economics of precision farming and conventional practices: evidences from KwaZulu-Natal Province, South Africa. Environment, Development and Sustainability, 1-21.

Vanitha, S. M., Chander, M., Kaur, N., & Fisher, G. (2017). Role and current status of precision agriculture in India. Sugar Tech, 19(5), 455-470.

Fields, C., Hoermann, G., Gindele, N., Gaiser, T., Häring, F., Schauberger, B., ... & Streck, T. (2020). Yield and economic effects of precision crop load adjusted irrigation and nitrogen application compared to conventional farm management. Agricultural Water Management, 240, 106273.

Msibi, Z. L., & Oladele, O. I. (2021). Comparative economics of precision farming and conventional practices: evidences from KwaZulu-Natal Province, South Africa. Environment, Development and Sustainability, 1-21.

Ngouajio, M., & Ernest Joshua, S. (2020). Technological advances toward improving global food security. In Food Security and Land Use Change Under Conditions of Climate Change (pp. 1-20). Springer, Cham.

Tripuraneni, V., Bizimana, J. C., Ogden, A. E., Richardson, J. W., Righette, R., Artioli, L., ... & Morgan, C. L. (2021). Rising atmospheric carbon dioxide concentration affects sorghum gas exchange and agronomic traits under variable irrigation. Frontiers in plant science, 12, 1454.

Behera, S. K., Nayak, D. K., Villano, R., Naik, P. K., Miro, B., & Nelson, A. (2021). Conservation Agriculture with Trees Boosts System Productivity of Rain-Fed Smallholder Farms in India. Plants 2021, 10, 1189.

Swain, D. K., & Sahoo, N. (2015). Assessing suitability of precision agricultural technologies using geographic information systems. Agropedology, 16, 18.

Hedley, C. B., & Yule, I. J. (2009). Soil water status mapping and two variable-rate irrigation scenarios. Precision Agriculture, 10(4), 342-355.

Auat Cheein, F. A., & Carelli, R. (2013). Analysis of different features of an autosteer guidance system running a vineyard row. Journal of Applied Computer Science & Mathematics, 7(6), 266-272.

Adamchuk, V. I., Hummel, J. W., Morgan, M. T., & Upadhyaya, S. K. (2004). On-the-go soil sensors for precision agriculture. Computers and electronics in agriculture, 44(1), 71-91.

[229] Timmermann, C. J., Gerhards, R., & Kühbauch, W. (2003). The economic impact of site-specific weed control. Precision Agriculture, 4(3), 249-260.

Maresma, Á., Ariza, M., Martínez, E., Lloveras, J., & Martínez-Casasnovas, J. A. (2016). Analysis of vegetation indices to determine nitrogen application and yield prediction in maize (Zea mays L.) from a standard uav service. Remote sensing, 8(12), 973.

López-Granados, F., Torres-Sánchez, J., Serrano-Pérez, A., de Castro, A. I., Mesas-Carrascosa, F. J., & Peña, J. M. (2016). Early season weed mapping with UAVs: A case study in sunflower. Precision Agriculture, 17(5), 594-612.

Bullock, D. G., Nielsen, R. L., Nyquist, W. E., & Oplinger, E. S. (2019). Tillage, crop rotation, and planting date effects on corn yield. Agronomy journal, 111(1), 138-154.

Gée, C., Bossu, J., Jones, G., & Truchetet, F. (2022). Reduction of pesticide use, opportunities, and acceptability for automatic weeding robots in arable crops. A review. Agronomy for sustainable development, 42(2), 1-24.

Vuran, M. C. (2010). Wireless underground sensor networks using commodity terrestrial motes (Doctoral dissertation). University of Nebraska, Lincoln, Nebraska, USA.

Qaim, M., & Zilberman, D. (2003). Yield effects of genetically modified crops in developing countries. Science, 299(5608), 900-902.

Roberts, D. C., Brorsen, B. W., Taylor, R. K., Solie, J. B., & Raun, W. R. (2012). Replicability of nitrogen recommendations from ramped calibration strips in winter wheat. Agronomy journal, 104(1), 26- 33.

Morellos, A., Pantazi, X. E., Moshou, D., Alexandridis, T., Whetton, R., Tziotzios, G., ... & Mouazen, A. M. (2016). Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy. Biosystems engineering, 152, 104-116.

Melchiori, R., Vandermeiren, K., Vanderborght, J., & Van Camp, L. M. (2018). Yield response and nitrogen use efficiency of drip-irrigated vegetable crops under precise fertilization using soil moisture sensors. Agricultural Water Management, 210, 163-170.

Rose, D. C., Sutherland, W. J., Barnes, A. P., Borthwick, F., Ffoulkes, C., Hall, C., ... & Dicks, L. V. (2019). Integrated farm management for sustainable agriculture: lessons for knowledge exchange and policy. Land Use Policy, 81, 834-842.