An interdisciplinary approach to artificial intelligence in agriculture

References

• Albiero, D. (2019). Agricultural robotics: A promising challenge. Current Agriculture Research Journal, 7(1), 1.
   Google Scholar
• Almeria, S., Lopez-Gatius, F., Garcia-Ispierto, I., Nogareda, C., Bech-Sabat, G., Serrano, B., Santolaria, P., & Yaniz, J. L. (2009). Effects of crossbreed pregnancies on the abortion risk of Neospora caninum-infected dairy cows. Veterinary parasitology, 163, 323–31.
   PubMed Web of Science ®Google Scholar
• Aggarwal, N., & Singh, D. (2021). Technology assisted farming: Implications of IoT and AI, in: IOP Conference Series: Materials Science and Engineering. IOP Publishing, p. 012080.
   Google Scholar
• Athey, S. (2018). The impact of machine learning on economics the economics of artificial intElligence: An Agenda ( pp. 507–547). National Bureau of Economic Research University of Chicago Press.
   Google Scholar
• Awasthi, Y. (2020). Press “a” for artificial intelligence in agriculture: A review. International Journal on Informatics Visualization, 4(3), 112–116.
   Google Scholar
• Bacco, M., Barsocchi, P., Ferro, E., Gotta, A., & Ruggeri, M. (2019). The digitisation of agriculture: A survey of research activities on smart farming. Array, 3, 100009.
   Google Scholar
• Basnet, B., & Bang, J. (2018). The state-of-the-art of knowledge-intensive agriculture: A review on applied sensing systems and data analytics. Journal of Sensors.
   Web of Science ®Google Scholar
• Bessen, J. E. (2016). ‘How computer automation affects occupations: Technology, jobs, and skills’, Boston Univ. school of law, law and economics research paper, 15–49.
   Google Scholar
• Bestelmeyer, B. T., Marcillo, G., McCord, S. E., Mirsky, S., Moglen, G., Neven, L. G., Peters, D., Sohoulande, C., & Wakie, T. (2020). Scaling up agricultural research with artificial intelligence. IT Professional, 22(3), 33–38.
   Web of Science ®Google Scholar
• Bogomolov, A., Nevezhin, V., Larionova, M. and Piskun, E. (2021). Review of digital technologies in agriculture as a factor that removes the growth limits to human civilization (p. 01074). EDP Sciences.
   Google Scholar
• Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
   Google Scholar
• Bryan, B. A. (2013). High-performance computing tools for the integrated assessment and modelling of social–ecological systems. Environmental Modelling & Software, 39, 295–303. ISSN 1364-8152. https://doi.org/10.1016/j.envsoft.2012.02.006
   Web of Science ®Google Scholar
• Camaréna, S. (2020). Artificial Intelligence in the design of transition to sustainable food systems. Journal of Cleaner Production, 271, 122574.
   Web of Science ®Google Scholar
• Carolan, M. (2020). Automated agrifood futures: Robotics, labor and the distributive politics of digital agriculture. The Journal of Peasant Studies, 47(1), 184–207.
   Web of Science ®Google Scholar
• Chavez, E., Conway, G., Ghil, M., & Sadler, M. (2015). An end-to-end assessment of extreme weather impacts on food security. Nature Clim. Change, 5, 997.
   Web of Science ®Google Scholar
• Chen, N., Zhang, X., & Wang, C. (2015). Integrated open geospatial web service enabled cyber-physical information infrastructure for precision agriculture monitoring. Computers and Electronics in Agriculture, 111, 78–91. https://doi.org/10.1016/j.compag.2014.12.009
   Web of Science ®Google Scholar
• Christos, P., Sjoukje, O., & Athanasiadis, I. N. (2021). Introducing digital twins to agriculture. Computers and Electronics in Agriculture, 184, 105942.
   Web of Science ®Google Scholar
• Ciruela-Lorenzo, A. M., Del-Aguila-Obra, A. R., Padilla-Meléndez, A., & Plaza-Angulo, J. J. (2020). Digitalization of agri-cooperatives in the smart agriculture context. Proposal of a digital diagnosis tool. Sustainability (Switzerland), 12(4), 1325.
   Web of Science ®Google Scholar
• Clifton, J., Glasmeier, A., & Gray, M. (2020). When machines think for us: The consequences for work and place. Cambridge Journal of Regions, Economy and Society, 13(1), 3–23.
   Web of Science ®Google Scholar
• Coble, K. H., Mishra, A. K., Ferrell, S., & Griffin, T. (2018). Big data in agriculture: A challenge for the future. Applied Economic Perspectives and Policy, 40(1), 79–96.
   Web of Science ®Google Scholar
• Cook, P., & O’Neill, F. (2020). Artificial intelligence in agribusiness is growing in emerging markets. Washington, D.C: EMCompass.
   Google Scholar
• Cubric, M. (2020). Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society, 62, 101257.
   Web of Science ®Google Scholar
• Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42.
   Web of Science ®Google Scholar
• Davenport, T. H. (2018). The AI advantage: How to put the artificial intelligence revolution to work. mit Press).
   Google Scholar
• Dharmaraj, V., & Vijayanand, C. (2018). Artificial intelligence (AI) in agriculture. International Journal of Current Microbiology and Applied Sciences, 7(12), 2122–2128.
   Google Scholar
• Dolfsma, W., Isakhanyan, G., & Wolfert, J. (2021). Information exchange in supply chains: The case of agritech. Journal of Economic Issues, 55(2), 389–396.
   Web of Science ®Google Scholar
• Duckett, T., Pearson, S., Blackmore, S., Grieve, B., Chen, W.H., Cielniak, G., Cleaversmith, J., Dai, J., Davis, S., Fox, C. and From, P. (2018). Agricultural robotics: The future of robotic agriculture. arXiv preprint arXiv:180606762.
   Google Scholar
• Eli-Chukwu, N. C. (2019). Applications of artificial intelligence in agriculture: A review. Engineering, Technology & Applied Science Research, 9(4), 4377–4383.
   Web of Science ®Google Scholar
• Esgario, J. G. M., Krohling, R. A., & Ventura, J. A. (2020). Deep learning for classification and severity estimation of coffee leaf biotic stress. Comput. Electron. Agric. 169
   Web of Science ®Google Scholar
• Fang, J., Zhang, C., & Wang, S. (2007). ‘Application of genetic algorithm (GA) trained artificial neural network to identify tomatoes with physiological diseases’, International Conference on Computer and Computing Technologies in Agriculture (Springer), 1103–1111.
   Google Scholar
• Ferrández-Pastor, F. J., García-Chamizo, J. M., & Nieto-Hidalgo, M. (2016). Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture. Sensors, 16, 1–20. https://doi.org/10.3390/s16071141
   Web of Science ®Google Scholar
• Franzen, D., & Mulla, D. (2015). A history of precision agriculture. Precision Agriculture Technology for Crop Farming, 1–20.
   Google Scholar
• Gallinucci, E., Golfarelli, M., & Rizzi, S. (2020). Mo.Re.farming: A hybrid architecture for tactical and strategic precision agriculture. Data & Knowledge Engineering, 129, 101836.
   Web of Science ®Google Scholar
• Gardezi, M., & Stock, R. (2021). Growing algorithmic governmentality: Interrogating the social construction of trust in precision agriculture. Journal of Rural Studies, 84, 1–11.
   Web of Science ®Google Scholar
• Gentili, A., Compagnucci, F., Gallegati, M., & Valentini, E. (2020). Are machines stealing our jobs? Cambridge Journal of Regions, Economy and Society, 13(1), 153–173.
   Web of Science ®Google Scholar
• Gerhards, R., & Christensen, S. (2003). Real‐time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley. Weed Research, 43(6), 385–392.
   Web of Science ®Google Scholar
• Ghaffarian, S., Valente, J., van der Voort, M., & Tekinerdogan, B. (2021). Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review. Remote Sensing, 13(15), 2965.
   Web of Science ®Google Scholar
• Haque, A., Islam, N., Samrat, N. H., Dey, S., & Ray, B. (2021). Smart farming through responsible leadership in Bangladesh: Possibilities, opportunities, and beyond. Sustainability (Switzerland), 13(8), 4511.
   Web of Science ®Google Scholar
• Hatfield, J. L., Cryder, M., & Basso, B. (2020). Remote sensing: Advancing the science and the applications to transform agriculture. IT Professional, 22(3), 42–45.
   Web of Science ®Google Scholar
• Isakhanyan, G., & Dolfsma, W. (2020). Naar grootschalige hightech-landbouw. Duurzame Landbouw, ESB, Economisch Statistische Berichten, N 4791S. https://esb.nu/incoming/20061418/esb-4791s-duurzame-landbouw
   Google Scholar
• Joshi, R., Banwet, D. K., & Shankar, R. (2011). A Delphi-AHP-TOPSIS based benchmarking framework for performance improvement of a cold chain. Expert Systems with Applications, 38(8), 10170–10182. https://doi.org/10.1016/j.eswa.2011.02.072
   Web of Science ®Google Scholar
• Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H., & Pasupuleti, V. R. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, 100033.
   Google Scholar
• Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). A review of the use of convolutional neural networks in agriculture. The Journal of Agricultural Science, 156(3), 312–322.
   Web of Science ®Google Scholar
• Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS-Wagening. J. Life Sci, 90, 100315.
   Web of Science ®Google Scholar
• Khoshnevisan, B., Rafiee, S., Pan, J., Zhang, Y., & Liu, H. (2020). A multi-criteria evolutionary-based algorithm as a regional scale decision support system to optimize nitrogen consumption rate; a case study in North China plain. Journal of Cleaner Production, 256, 120213.
   Web of Science ®Google Scholar
• Knight, J. C. (2002). Safety critical systems: Challenges and directions, Proceedings of the 24th International Conference on Software Engineering. ICSE 2002, 2002, pp. 547–550.
   Google Scholar
• Kolhe, S., Kamal, R., Saini, H. S., & Gupta, G. K. (2011). A web-based intelligent disease-diagnosis system using a new fuzzy-logic based approach for drawing the inferences in crops. Computers and Electronics in Agriculture, 76(1), 16–27.
   Web of Science ®Google Scholar
• Krishnan, A., & Swarna, S. (2020). Robotics, IoT, and AI in the Automation of Agricultural Industry: A Review ( pp. 1–6). IEEE).
   Google Scholar
• Krisnawijaya, N. N. K., Tekinerdogan, B., Catal, C., & van der Tol, R. (2022). Data analytics platforms for agricultural systems: A systematic literature review. Computers and Electronics in Agriculture, 195, 106813.
   Web of Science ®Google Scholar
• Lal, H., Jones, J. W., Peart, R. M., & Shoup, W. D. (1992). FARMSYS—A whole-farm machinery management decision support system. Agricultural systems, 38(3), 257–273.
   Web of Science ®Google Scholar
• Lassoued, R., Macall, D. M., Smyth, S. J., Phillips, P. W. B., & Hesseln, H. (2021). Expert insights on the impacts of, and potential for, agricultural big data. Sustainability (Switzerland), 13(5), 1–18.
   Web of Science ®Google Scholar
• Lakshmi, V., & Bahli, B. (2020). Understanding the robotization landscape transformation: A centering resonance analysis. Journal of Innovation and Knowledge, 5(1), 59–67.
   Web of Science ®Google Scholar
• Lajoie O'Malley, A., Bronson, K., van der Burg, S., & Klerkx, L. (2020). The future (s) of digital agriculture and sustainable food systems: An analysis of high-level policy documents. Ecosystem Services, 45, 101183.
   Web of Science ®Google Scholar
• Legun, K., & Burch, K. (2021). Robot-ready: How apple producers are assembling in anticipation of new AI robotics. Journal of Rural Studies, 82, 380–390.
   Web of Science ®Google Scholar
• Lezoche, M., Hernandez, J. E., Alemany Díaz, M. D. M. E., Panetto, H., & Kacprzyk, J. (2020). Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture. Computers in Industry, 117, 103187.
   Web of Science ®Google Scholar
• Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
   PubMed Web of Science ®Google Scholar
• Liu, S. Y. (2020). Artificial intelligence (AI) in agriculture. IT Professional, 22(3), 14–15.
   Web of Science ®Google Scholar
• Lokers, R., Knapen, R., Janssen, S., van Randen, Y., & Jansen, J. (2016). Analysis of Big Data technologies for use in agro-environmental science. Environmental Modelling & Software, 84, 494–504.
   Web of Science ®Google Scholar
• Mark, R. (2019). Ethics of using AI and big data in agriculture: The case of a large agriculture multinational. The ORBIT Journal, 2(2), 1–27.
   Google Scholar
• Marvin, H. J. P., Bouzembrak, Y., van der Fels-Klerx, H. J., Kempenaar, C., Veerkamp, R., Chauhan, A., Stroosnijder, S., Top, J., Simsek-Senel, G., Vrolijk, H., Knibbe, W. J., Zhang, L., Boom, R., & Tekinerdogan, B. (2022). Digitalisation and artificial intelligence for sustainable food systems. Trends in Food Science & Technology, 120, 344–348. https://doi.org/10.1016/J.TIFS.2022.01.020
   Web of Science ®Google Scholar
• Meshram, V., Patil, K., Meshram, V., Hanchate, D., & Ramkteke, S. D. (2021). Machine learning in agriculture domain: A state-of-art survey. Artificial Intelligence in the Life Sciences, 1, 100010.
   Google Scholar
• Mhlanga, D. (2021). Artificial intelligence in the industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies?’. Sustainability (Switzerland), 13(11), 5788.
   Web of Science ®Google Scholar
• Penning, B. W., Snelling, W. M., & Woodward-Greene, M. J. (2020). Machine learning in the assessment of meat quality. IT Professional, 22(3), 39–41.
   Web of Science ®Google Scholar
• Perakis, K., Lampathaki, F., Nikas, K., Georgiou, Y., Marko, O., & Maselyne, J. (2020). Cybele–fostering precision agriculture & livestock farming through secure access to large-scale HPC enabled virtual industrial experimentation environments fostering scalable big data analytics. Computer Networks, 168, 107035.
   Web of Science ®Google Scholar
• Pilarski, T., Happold, M., Pangels, H., Ollis, M., Fitzpatrick, K., & Stentz, A. (2002). The demeter system for automated harvesting. Autonomous robots, 13(1), 9–20.
   Google Scholar
• Picon, A., Alvarez-Gila, A., Seitz, M., Ortiz-Barredo, A., Echazarra, J., & Johannes, A. (2019). Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Computers and Electronics in Agriculture, 161, 280–290.
   Web of Science ®Google Scholar
• Plant, R. E. (1989). An artificial intelligence based method for scheduling crop management actions. Agricultural systems, 31(1), 127–155.
   Web of Science ®Google Scholar
• Rodzalan, S. A., Yin, O. G., & Noor, N. N. M. (2020). A foresight study of artificial intelligence in the agriculture sector in Malaysia. Journal of Critical Reviews, 7(8), 1339–1346.
   Google Scholar
• Rose, D. C., Lyon, J., de Boon, A., Hanheide, M., & Pearson, S. (2021). Responsible development of autonomous robotics in agriculture. Nat. Food, 2, 306–309.
   PubMedGoogle Scholar
• Rose, D. C., & Chilvers, J. (2018). Agriculture 4.0: Broadening responsible innovation in an era of smart farming. Frontiers in Sustainable Food Systems, 2, 87.
   Google Scholar
• Rotz, S., Gravely, E., Mosby, I., Duncan, E., Finnis, E., Horgan, M., LeBlanc, J., Martin, R., Neufeld, H. T., Nixon, A., Pant, L., Shalla, V., & Fraser, E. (2019). Automated pastures and the digital divide: How agricultural technologies are shaping labour and rural communities. Journal of Rural Studies, 68, 112–122.
   Web of Science ®Google Scholar
• Ruiz-Real, J. L., Uribe-Toril, J., Torres Arriaza, J. A., & de Pablo Valenciano, J. (2020). A look at the past, present and future research trends of artificial intelligence in agriculture. Agronomy, 10(11), 1839.
   Google Scholar
• Ryan, M. (2019a). Agricultural big data analytics and the ethics of power. Journal of Agricultural & Environmental Ethics, 33, 49–69.
   Web of Science ®Google Scholar
• Ryan, M. (2019b). Ethics of using AI and big data in agriculture: The case of a large agriculture multinational. ORBIT Journal, 2(2), 1–27.
   Google Scholar
• Ryan, M. (2022). The social and ethical impacts of artificial intelligence in agriculture: Mapping the agricultural AI literature ( pp. 1–13). AI & SOCIETY.
   Google Scholar
• Ryan, M., van der Burg, S., & Bogaardt, M.J. (2021). Identifying key ethical debates for autonomous robots in agri-food: A research agenda. AI and Ethics, 2, 493–507.
   Google Scholar
• Schimmelpfennig, D., & Ebel, R. (2016). Sequential adoption and cost savings from precision agriculture. Journal of Agricultural and Resource Economics, 97–115.
   Web of Science ®Google Scholar
• Seo, Y., & Umeda, S. (2021). Evaluating farm management performance by the choice of pest-control sprayers in rice farming in Japan. Sustainability (Switzerland), 13(5), 1–10.
   Web of Science ®Google Scholar
• Shepherd, M., Turner, J. A., Small, B., & Wheeler, D. (2020). Priorities for science to overcome hurdles thwarting the full promise of the ‘digital agriculture’revolution. Journal of the Science of Food and Agriculture, 100(14), 5083–5092.
   PubMed Web of Science ®Google Scholar
• Singh, S., Chana, I., & Buyya, R. (2020). Agri-Info: Cloud Based Autonomic System for Delivering Agriculture as a Service Data Analytics. Internet of Things, 9, 1–16.
   Google Scholar
• Silva, S., Alçada-Almeida, L., & Dias, L. C. (2014). Development of a Web-based Multi-criteria Spatial Decision Support System for the assessment of environmental sustainability of dairy farms. Computers and Electronics in Agriculture, 108, 46–57. https://doi.org/10.1016/j.compag.2014.06.009
   Web of Science ®Google Scholar
• Sood, A., Sharma, R. K., & Bhardwaj, A. K. (2022). Artificial intelligence research in agriculture: A review. Online Information Review, 46(6), 1054–1075.
   Web of Science ®Google Scholar
• Smith, M. J. (2020). Getting value from artificial intelligence in agriculture. Animal Production Science, 60(1), 46–54.
   Web of Science ®Google Scholar
• Sparrow, R., Howard, M., & Degeling, C. (2021). Managing the risks of artificial intelligence in agriculture. NJAS: Impact in Agricultural and Life Sciences, 93(1), 172–196.
   Google Scholar
• Sparrow, R., & Howard, M. (2020). Robots in agriculture: Prospects, impacts, ethics, and policy. Precision Agriculture, 22, 1–16.
   Web of Science ®Google Scholar
• Stock, R., & Gardezi, M. (2021). Make bloom and let wither: Biopolitics of precision agriculture at the dawn of surveillance capitalism. Geoforum, 122, 193–203.
   Web of Science ®Google Scholar
• Taneja, M., Byabazaire, J., Jalodia, N., Davy, A., Olariu, C., & Malone, P. (2020). Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle. Computers and Electronics in Agriculture, 171.
   Web of Science ®Google Scholar
• Triantafyllou, A., Sarigiannidis, P., & Bibi, S. (2019). Precision Agriculture: A Remote Sensing Monitoring System Architecture. Information, 10, 1–25. https://doi.org/10.3390/info10110348
   Google Scholar
• Thomson, A. J., & Willoughby, I. (2004). A web-based expert system for advising on herbicide use in great britain. Computers and Electronics in Agriculture, 42(1), 43–49.
   Web of Science ®Google Scholar
• Tobal, A. (2014). Weeds identification using evolutionary artificial intelligence algorithm. Journal of Computer Science, 10(8), 1355–1361.
   Google Scholar
• Udupi, V. R. (2019). Identification of soybean diseases using learning vector quantization neural network algorithm. Journal of Analysis and Computation (JAC), 1(1), 1–3.
   Google Scholar
• Vasconez, J. P., Kantor, G. A., & Cheein, A. (2019). Human–robot interaction in agriculture: A survey and current challenges. Biosystems Engineering, 179, 35–48.
   Web of Science ®Google Scholar
• Viktor, S., Tímea, C., & János, A. (2021). The applicability of big data in climate change research: The importance of system of systems thinking. Frontiers in Environmental Science, 9. https://doi.org/10.3389/fenvs.2021.619092
   Web of Science ®Google Scholar
• Vernaza, P., & Rhinehart, N. (2019), ‘Traffic prediction with reparameterized push forward policy for autonomous vehicles’, (Google Patents).
   Google Scholar
• Wang, T., Xu, X., Wang, C., Li, Z., & Li, D. (2021). From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production. Agriculture, 11, 145.
   Google Scholar
• Weersink, A., Fraser, E., Pannell, D., Duncan, E., & Rotz, S. (2018). Opportunities and challenges for big data in agricultural and environmental analysis. Annual Review of Resource Economics, 10(1), 19–37.
   Web of Science ®Google Scholar
• Wolfert, S., Goense, D., & Sørensen, C. A. G. (2014), ‘A future internet collaboration platform for safe and healthy food from farm to fork’, 2014 annual SRII global conference (IEEE), 266–273.
   Google Scholar
• Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural systems, 153, 69–80.
   Web of Science ®Google Scholar
• Yang, C.-C., Prasher, O. S., Landry, J., & Ramaswamy, S. H. (2002). Development of neural networks for weed recognition in corn fields. Transactions of the ASAE, 45(3), 859.
   Google Scholar
• Zhao, Z., Chow, T. L., Rees, H. W., Yang, Q., Xing, Z., & Meng, F.-R. (2009). Predict soil texture distributions using an artificial neural network model. Computers and Electronics in Agriculture, 65(1), 36–48.
   Web of Science ®Google Scholar
• Zhong, X., & Zhou, S. (2020). Risk analysis method of bank microfinance based on multiple genetic artificial neural networks. Neural Computing & Applications, 32, 5367–5377.
   Web of Science ®Google Scholar