The Risks of AI in Agriculture

Authors

DOI:

https://doi.org/10.14232/analecta.2024.4.32-44

Keywords:

artificial intelligence, agriculture, dangers, risks

Abstract

Integration of artificial intelligence (AI) into agriculture has the potential to revolutionise agriculture, but it also presents challenges and risks that must be carefully managed. AI can improve planning, streamline work processes, and improve decision making in crop cultivation and animal husbandry, ultimately leading to higher returns for farmers. However, lack of training and high implementation costs can make it difficult for some farmers to adopt AI, creating a competitive disadvantage and concentrating agricultural resources. Additionally, AI may contribute to unemployment among those with lower skill levels and poses cybersecurity risks that need continuous monitoring. Legal concerns also arise with respect to data ownership and usage rights, with questions about who can access and utilise collected data. Farmers often have to rely on AI systems as "black boxes", with limited understanding of how they work. If these systems fail and cause damage, accountability becomes an important issue. It is crucial to assess the drawbacks and risks of AI implementation in agriculture and educate farmers about these risks to prevent significant damage. Managing these risks effectively and ensuring data accuracy and security are essential in the global adoption of AI in agriculture.

Downloads

Download data is not yet available.

References

Cs. Köpöncei, A mezőgazdaságban is hódít a mesterséges intelligencia / Artificial intelligence is also conquering agriculture, Világgazdaság, 2023. (Accessed: 8th March, 2024, https://www.vg.hu/vilaggazdasag/2023/02/a-mezogazdasagban-is-hodit-a-mesterseges-intelligencia)

M. Czékus, Mesterséges intelligenciával támogatott mezőgazdaság / Agriculture supported by artificial intelligence, Mezőhír, 2021/5, 2021. (Accessed: 8th March, 2024, https://mezohir.hu/2021/05/10/mesterseges-intelligencia-az-agrariumban-mezogazdasag/)

Gy. Bőgel, A dolgok internetének hatása az ellátási láncokra: a mezőgazdaság példája / The impact of the Internet of Things on supply chains: the example of agriculture, Logisztikai trendek és legjobb gyakorlatok, 4 (2) (2018), pp. 23-27. https://doi.org/10.21405/logtrend.2018.4.2.23

N. C. Eli-Chukwu, Applications of Artificial Intelligence in Agriculture: A Review, Engineering, Technology & Applied Science Research, 9 (4) (2019), pp. 4377-4383. https://doi.org/10.48084/etasr.2756

S. Qazi, B. A. Khawaja, Q. U. Farooq, IoT-Equipped and AI-Enabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends, IEEE Access, 10 (2022), pp. 21219-21235. https://doi.org/10.1109/ACCESS.2022.3152544

M. C. Annosi, F. Brunetta, A. Monti, F. Nat, Is the trend your friend? An analysis of technology 4.0 investment decisions in agricultural SMEs, Computers in Industry, 109 (2019), pp. 59-71. https://doi.org/10.1016/j.compind.2019.04.003

V. Nagy, V. Hajdu, A mesterséges intelligencia lehetséges hatása(i) a „munka világára” / Potential Impact(s) of Artificial Intelligence on the 'World of Work', Jelenkori társadalmi és gazdasági folyamatok, 16 (1-2) (2021), pp. 79-90. https://doi.org/10.14232/jtgf.2021.1-2.79-90

European Parliament, What is artificial intelligence and how is it used?, 2023. (Accessed: 8th March, 2024, https://www.europarl.europa.eu/topics/en/article/20200827STO85804/what-is-artificial-intelligence-and-how-is-it-used ljak)

T. Stonier, The Evolution of Machine Intelligence, In: Stoner, T. (ed.), Beyond Information, Springer, London. 1992, 107-133. https://doi.org/10.1007/978-1-4471-1835-0_6

I. Szabó, Az agrárinformatika helyzete, fejlődési irányai hazánkban és nemzetközi kitekintésben / The situation and development directions of agricultural informatics in Hungary and in an international perspective, Állattenyésztés és takarmányozás, 68 (3) (2019), pp. 185-194.

A. Szalavetz, Mesterséges intelligencia és technológiavezérelt termelékenységemelkedés / Artificial intelligence and technology-driven productivity growth, Külgazdaság, 63 (7-8) (2019), pp. 53-79. (Accessed: 8th March, 2024, http://real.mtak.hu/102427/1/Szalavetz_MI_Final.pdf)

S. Russel, P. Norvig, Artificial Intelligence: A Modern Approach, Third Edition, Pearson Education Limited, New Jersey, 2010. (Accessed: 8th March, 2024, https://people.engr.tamu.edu/guni/csce421/files/AI_Russell_Norvig.pdf)

M. Taddy, The technological elements of artificial intelligence, National Bureau of Economic Research, Working Papers No. 24301 (2018). https://doi.org/10.3386/w24301

M. Chui, J. Manyika, M. Miremadi, N. Henke, R. Chung, P. Nel, S. Malhotra, Notes from the AI frontier: Applications and value of deep learning, McKinsey Global Institute, 2018. (Accessed: 8th March, 2024, https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-applications-and-value-of-deep-learning)

SAP, What is artificial intelligence?, 2023. (Accessed: 8th March, 2024, https://www.sap.com/uk/products/artificial-intelligence/what-is-artificial-intelligence.html)

N. Duggal, Advantages and Disadvantages of Artificial Intelligence [AI], Simpilearn, 2023. (Accessed: 8th March, 2024, https://www.simplilearn.com/advantages-and-disadvantages-of-artificial-intelligence-article)

E. Alreshidi, Smart Sustainable Agriculture (SSA) Solution Underpinned by Internet of Things (IoT) and Artificial Intelligence (AI), International Journal of Advanced Computer Science and Applications, 10 (5) (2019), pp. 93-102. (Accessed: 8th March, 2024, https://arxiv.org/ftp/arxiv/papers/1906/1906.03106.pdf)

L. Columbus, 10 Ways AI Has The Potential To Improve Agriculture In 2021, Forbes, 2021. (Accessed: 8th March, 2024, https://www.forbes.com/sites/louiscolumbus/2021/02/17/10-ways-ai-has-the-potential-to-improve-agriculture-in-2021/)

L. Morgan, AI examples that can be used effectively in agriculture, Techtarget Network, 2022. (Accessed: 8th March, 2024, https://www.techtarget.com/searchenterpriseai/feature/AI-examples-that-can-be-used-effectively-in-agriculture)

P. R. Bhagat, F. Naz, R. Magda, Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis, PLoS ONE, 17 (6) (2022), pp. 1-19: e0268989. https://doi.org/10.1371/journal.pone.0268989

W. Gonzalez, How AI Is Cropping Up In The Agriculture Industry? Forbes, 2023. (Accessed: 8th March, 2024, https://www.forbes.com/sites/forbesbusinesscouncil/2023/02/02/how-ai-is-cropping-up-in-the-agriculture-industry/)

R. Sparrow, M. Howard, Ch. Degeling, Managing the risks of artificial intelligence in agriculture, NJAS: Impact in Agricultural and Life Sciences, 93 (1) (2021), pp. 172-196. https://doi.org/10.1080/27685241.2021.2008777

M. Taylor, Researchers Warn of Risks of Using AI in Agriculture, Laboratory Equipment, News, 2022. (Accessed: 8th March, 2024, https://www.laboratoryequipment.com/583705-Researchers-Warn-of-Risks-of-Using-AI-in-Agriculture/)

BMEL – Bundesministerium für Ernährung und Landwirtschaft (ed.), Digitalisierung in der Landwirtschaft 2018, Bundesministerium für Ernährung und Landwirtschaft, 2018. (Accessed: 8th March, 2024, https://www.bmel.de/SharedDocs/Downloads/DE/Broschueren/digitalpolitik-landwirtschaft.pdf?__blob=publicationFile&v=9)

S. Wolfert, L. Ge, C. Verdouw, M.-J. Bogaardt, Big Data in Smart Farming – A review, Agricultural Systems, 153 (2017), pp. 69-80. https://doi.org/10.1016/j.agsy.2017.01.023

V. Bonneau, B. Copigneaux, L. Probst, B. Pedersen, Industry 4.0 in agriculture: Focus on IoT aspects. Digital Transformation Monitor, European Committee, 2017. (Accessed: 8th March, 2024, https://ati.ec.europa.eu/sites/default/files/2020-07/Industry%204.0%20in%20Agriculture%20-%20Focus%20on%20IoT%20aspects%20%28v1%29.pdf)

A. Deter, Landwirtschaft 4.0 – endlich mal praktisch, Top Agrar, (3) (2018), pp. 116–117. (Accessed: 8th March, 2024, https://www.topagrar.com/technik/news/landwirtschaft-4-0-endlich-mal-praktisch-9372736.html)

M. Kunisch, F. Kloepfer, Landwirtschaft 4.0 im Maisanbau. Mais, 44 (4) (2017), pp. 156-160.

FEFAC, EU Code of concuct on agricultural data sharing by contractual aggreement 2018, 2018, (Accessed: 8th March, 2024, https://fefac.eu/wp-content/uploads/2020/07/eu_code_of_conduct_on_agricultural_data_sharing-1.pdf)

B. Pollmann, Digitale Landwirtschaft: IT für Acker und Stall, 2017. (Accessed: 8th March, 2024, https://biooekonomie.de/digitale-landwirtschaft-it-fuer-acker-und-stall)

European Parliament, Artificial intelligence: threats and opportunities, 2020. (Accessed: 8th March, 2024, https://www.europarl.europa.eu/topics/en/article/20200918STO87404/artificial-intelligence-threats-and-opportunities)

I. Eisenberger, E. Hödl, A. Huber, K. Lachmayer, B. Mittermüller, „Smart Farming” – Rechtliche Perspektiven, In: Norer, R., Holzer, G. (eds.), Agrarrecht. Jahrbuch. NWV Verlag, Vienna, 2017, pp. 207-223.

B. Zsótér, D. Deák, Á. Búrány, Gy. Hampel, Ethical aspects of inventory management in an agricultural enterprise, Analecta Technica Szegedinensia, 13 (4) (2023), pp. 40-45. https://doi.org/10.14232/analecta.2023.4.40-45

K. Rass, Mesterséges intelligencia: áldás vagy átok? / Artificial intelligence: blessing or curse? Magyar mezőgazdaság, 2023. (Accessed: 8th March, 2024, https://magyarmezogazdasag.hu/2023/06/05/mesterseges-intelligencia-aldas-vagy-atok/)

J. Burrell, How the machine ‘thinks’: Understanding opacity in machine learning algorithms, Big Data and Society, 3 (1) (2016), pp. 1-12. https://doi.org/10.1177/2053951715622512

European Commission, High-level expert group on artificial intelligence, 2022. (Accessed: 8th March, 2024, https://digital-strategy.ec.europa.eu/en/policies/expert-group-ai)

A. Matthias, The responsibility gap: Ascribing responsibility for the actions of learning automata, Ethics and Information Technology, 6 (3) (2004), pp. 175-183.

R. Sparrow, Killer robots, Journal of Applied Philosophy, 24 (1) (2007), pp. 62-77. https://doi.org/10.1111/j.1468-5930.2007.00346.x

D. G. Johnson, Technology with no human responsibility?, Journal of Business Ethics, 127 (4) (2015), pp. 707-715. https://doi.org/10.1007/s10551-014-2180-1

L. Wiseman, T. Cockburn, J. Sanderson, Legal consequences of autonomous farming, Farm Policy Journal, 15 (2) (2018), pp. 37-46.

H. Murphy, AI, a new tool for cyber attackers - or defenders?, Financial Times Special Report – Navigating Cyber Risk. (Accessed: 8th March, 2024, https://www.ft.com/content/09d163be-0a6e-48f8-8185-6e1ba1273f42)

A. Tzachor, M. Devare, B. King, Sh. Avin, S. Ó hÉigeartaigh, Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities, Nature Machine Intelligence, 4 (2) (2022), pp. 104-109. https://doi.org/10.1038/s42256-022-00440-4

R. A. Clarke, R. Knake, Cyber war: The next threat to national security and what to do about it. Harper Collins, New York, 2010.

R. Dara, S. M. Hazrati Fard, J. Kaur, Recommendations for ethical and responsible use of artificial intelligence in digital agriculture, Frontiers in Artificial Intelligence, 5 (2022), pp. 884192. https://doi.org/10.3389/frai.2022.884192

D. Lenniy, AI in Agriculture The Future of Farming, 2023. (Accessed: 8th March, 2024, https://intellias.com/artificial-intelligence-in-agriculture/)

V. De Stefano, Negotiating the algorithm: Automation, artificial intelligence and labour protection. Working paper, International Labor Office, 2018. (Accessed: 8th March, 2024, https://www.ilo.org/wcmsp5/groups/public/---ed_emp/---emp_policy/documents/publication/wcms_634157.pdf)

D. C. Slaughter, D. K. Giles, D. Downey, Autonomous robotic weed control systems: A review, Computers and Electronics in Agriculture, 61 (1) (2008), pp. 63-78. https://doi.org/10.1016/j.compag.2007.05.008

C. L. McCarthy, N. H. Hancock, S. R. Raine, Applied machine vision of plants: A review with implications for field deployment of automated farming operations, Intelligent Service Robotics, 3 (4) (2010), pp. 209-217. https://doi.org/10.1007/s11370-010-0075-2

M. Keogh, M. Henry, The implications of digital agriculture and big data for australian agriculture, Australian Farm Institute, Sunny Hills NSW, 2016.

J. Bridle, New dark age: Technology and the end of the future, Verso Books, London, 2018.

V. Szőke, L. Kovács, Mezőgazdaság 4.0 – relevancia, lehetőségek, kihívások / Agriculture 4.0 – relevance, opportunities, challenges, Gazdálkodás, 64 (4) (2020), pp. 289-304.

V. Galaz, M. A. Centeno, P. W. Callahan, A. Causevic, Th. Patterson, I. Brass, S. Baum, D. Farber, J. Fischer, D. Garcia, T. McPhearson, D. Jimenez, B. King, P. Larcey, K. Levy, Artificial intelligence, systemic risks, and sustainability, Technology in Society, 67 (2021), pp. 101741. https://doi.org/10.1016/j.techsoc.2021.101741

L. Barangé, Artificial Intelligence: How could it transform agriculture?, 2023, (Accessed: 8th March, 2024, https://alliancebioversityciat.org/stories/artificial-intelligence-agriculture)

A. Fleming, E. Jakku, L. Lim-Camacho, B. Taylor, P. Thorburn, Is big data for big farming or for everyone? Perceptions in the Australian grains industry, Agronomy for Sustainable Development, 38 (3) (2018), pp. 24-34. https://doi.org/10.1007/s13593-018-0501-y

Cropin, Ethical and Safe AI In Agriculture: Considerations for Lending & Insurance, 2021. (Accessed: 8th March, 2024, https://www.cropin.com/blogs/ethical-and-safe-ai-in-agriculture-considerations-for-lending-insurance)

D. Autor, F. Levy, R. J. Murnane, The skill content of recent technological change: An empirical exploration, The Quarterly Journal of Economics, 118 (4) (2003), pp. 1279-1333. https://doi.org/10.1162/003355303322552801

C. B. Frey, M. A. Osborne, The future of employment: How susceptible are jobs to computerisation?, Technological Forecasting and Social Change, 114 (2017), pp. 254-280. https://doi.org/10.1016/j.techfore.2016.08.019

E. Brynjolfsson, A. McAfee, The second machine age: Work, progress, and prosperity in a time of brilliant technologies, W W Norton & Co., New York, 2014.

S. E. Bell, A. Hullinger, L. Brislen, Manipulated masculinities: Agribusiness, deskilling, and the rise of the businessman-farmer in the United States, Rural Sociology, 80 (3) (2015), pp. 285-313. https://doi.org/10.1111/ruso.12066

N. Carr, The glass cage: How our computers are changing us, W W Norton & Co., New York, 2015.

S. Rotz, E. Gravely, I. Mosby, E. Duncan, E. Finnis, M. Horgan, J. LeBlanc, R. Martin, H. T. Neufeld, A. Nixon, L. Pant, V. Shalla, E. Fraser, Automated pastures and the digital divide: How agricultural technologies are shaping labour and rural communities, Journal of Rural Studies, 68 (2019), pp. 112-122. https://doi.org/10.1016/j.jrurstud.2019.01.023

B. Zsótér, Turizmus Mezőhegyesen: a Hotel Nonius bemutatása / Tourism in Mezőhegyes: presentation of Hotel Nonius, In: J. Gál (ed.), Európai Uniós Kutatási és Oktatási Projektek Napja és Leonardo da Vinci Learn at Work Projekt-találkozó, Delfin Computer Informatikai Zrt., Hódmezővásárhely, 2006.

H. Gosnell, J. Abrams, Amenity migration: Diverse conceptualizations of drivers, socioeconomic dimensions, and emerging challenges, GeoJournal, 76 (4) (2011), pp. 303-322. https://doi.org/10.1007/s10708-009-9295-4

L. Klerkx, E. Jakku, P. Labarthe, A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda, NJAS – Wageningen Journal of Life Sciences, 100315 (2019), pp. 90-91. https://doi.org/10.1016/j.njas.2019.100315

B. Zsótér, S. Illés, P. Simonyi, Model of Local Economic Development in Hungarian Countryside, European Countryside, 12 (1) (2020), pp. 85-98. https://doi.org/10.2478/euco-2020-0005

University of Cambridge, Risks of using AI to grow our food are substantial and must not be ignored, warn researchers, ScienceDaily, 2022. (Accessed: 8th March, 2024, https://www.sciencedaily.com/releases/2022/02/220223111240.htm)

D. Tilman, Global environmental impacts of agricultural expansion: The need for sustainable and efficient practices, Proceedings of the National Academy of Sciences, 96 (11) (1999), pp. 5995-6000. https://doi.org/10.1073/pnas.96.11.5995

J. Ellul, The technological society, Vintage Books, New York, 1964.

M. Heidegger, The question concerning technology, and other essays, Garland Publishing, New York, 1977.

A. Woods, Rethinking the history of modern agriculture: British pig production, ca. 1910–65, Twentieth Century British History, 23 (2) (2012), pp. 165-191. https://doi.org/10.1093/tcbh/hwr010

L. Holloway, C. Bear, K. Wilkinson, Re-capturing bovine life: Robot–cow relationships, freedom and control in dairy farming, Journal of Rural Studies, 33 (2014), pp. 131-140. https://doi.org/10.1016/j.jrurstud.2013.01.006

L. Holloway, C. Bear, K. Wilkinson, Robotic milking technologies and renegotiating situated ethical relationships on UK dairy farms, Agriculture and Human Values, 31 (2) (2014), pp. 185-199. https://doi.org/10.1007/s10460-013-9473-3

T. Nemes, Mesterséges intelligencia: már jövőre kidurranhat a lufi / Artificial intelligence: the balloon may burst as early as next year, Világgazdaság, 2023. (Accessed: 8th March, 2024, https://www.vg.hu/penz-es-tokepiac/2023/10/mesterseges-intelligencia-mar-jovore-kidurranhat-a-lufi)

Downloads

Published

2024-12-12 — Updated on 2024-12-17

How to Cite

Hampel, G., & Fabulya, Z. (2024). The Risks of AI in Agriculture. Analecta Technica Szegedinensia, 18(4), 32–44. https://doi.org/10.14232/analecta.2024.4.32-44

Issue

Section

Articles