Artificial Intelligence is Clashing with Agriculture Law

by Martin Medeiros, Buckley Law, P.C.

Artificial Intelligence (AI) is increasingly intersecting with the agricultural sector, bringing both transformative benefits and significant legal challenges. The integration of AI technologies in agriculture, particularly through precision farming, automated machinery, and predictive analytics, aims to enhance productivity, efficiency, and sustainability in response to growing global food demands and the pressing challenges of climate change.[i] [ii] [iii]. However, this rapid technological advancement is also clashing with existing agricultural laws, leading to complex issues surrounding liability, data privacy, and regulatory compliance that require urgent attention and resolution.[iv] [v]

One of the primary legal concerns relates to liability in the context of autonomous agricultural systems. As AI-driven machinery becomes commonplace, determining liability in the event of accidents raises questions about the responsibilities of manufacturers, operators, and software developers, complicating legal claims and defenses.[vi]. Furthermore, the implementation of AI raises significant data privacy issues, as various state laws require robust data protection assessments to safeguard consumer rights amidst automated decision-making processes.[vii] [viii]

Ethical considerations also loom large in the debate over AI in agriculture. The potential for economic inequality, particularly in developing regions, exacerbates concerns about how automation might displace human labor and worsen livelihoods in communities reliant on traditional farming practices.[ix] [x] This may have labor law compliance issues.

As stakeholders navigate these multifaceted challenges, the development of coherent legal frameworks and ethical guidelines is essential to ensure responsible AI adoption in agriculture that prioritizes sustainability, equality and efficiency. [xi] [xii]

The clash between AI technology and agriculture law signifies a critical juncture in the evolution of farming practices. As the agricultural sector continues to adopt innovative AI solutions, the legal landscape must adapt accordingly, ensuring that technology’s benefits can be harnessed by lawyers while addressing the accompanying risks and ethical dilemmas.

Historical Context

Agricultural technology has undergone significant transformation over the centuries, evolving from traditional farming practices to advanced, data-driven systems that utilize artificial intelligence (AI) and automation. The journey of agricultural technology began with early innovations such as the plow and irrigation techniques, which fundamentally enhanced farming efficiency and productivity. These foundational advancements paved the way for the mechanization of agriculture during the 20th century, marked by the introduction of machinery like tractors and the chemical application of fertilizers and pesticides, which revolutionized crop production methods. As we entered the 21st century, the pressing challenges of climate change and increasing global population began to demand more sustainable and efficient agricultural practices. In response, technologies such as precision farming emerged, harnessing data analytics to optimize resource allocation and minimize environmental impact. This modern approach incorporates various AI applications, including crop and soil monitoring, predictive analytics, and automated machinery, significantly shaping contemporary farming operations. The integration of AI into agriculture aims not only to enhance productivity and efficiency but also to address the urgent need for sustainability in food production. For example, initiatives by organizations such as the Food and Agriculture Organization (FAO), in partnership with tech giants like IBM and Microsoft, have emphasized the importance of developing AI systems that promote food and nutrition security while adhering to ethical principles. This underscores a broader recognition of the role technology plays in achieving sustainable agricultural practices amidst the challenges posed by depleting natural resources and evolving climatic conditions.

Current Applications of AI in Agriculture

Artificial Intelligence (AI) is revolutionizing the agricultural sector through various innovative applications that enhance productivity, efficiency, and sustainability. The integration of AI technologies is particularly critical in addressing the challenges posed by rapid population growth and increasing food demand, as traditional farming methods often fall short in meeting these needs without resorting to harmful practices.

Precision Agriculture

One of the most prominent applications of AI in agriculture is precision farming, which utilizes advanced technologies like sensors, drones, and robotics to optimize farming practices. This approach enables farmers to monitor soil health, moisture levels, and crop conditions in real-time, allowing for more efficient resource allocation and management. AI-driven systems can analyze data to provide actionable insights, ensuring that water, fertilizers, and pesticides are applied in the right amounts at the right times.[xiii] [xiv]

Automated Machinery

The deployment of automated machinery has transformed various agricultural tasks, reducing the need for manual labor and increasing efficiency. AI-powered robots and autonomous tractors are now capable of performing planting, weeding, and harvesting with minimal human intervention. These innovations not only save time but also enhance precision in farming operations, leading to higher yields and better resource management.

Pest and Disease Detection

AI technologies are also being used for early pest and disease detection. By leveraging image recognition systems, farmers can identify issues such as plant diseases and pest infestations early, enabling timely preventive measures that minimize crop loss. This capability is crucial for protecting crop health and reducing reliance on harmful chemicals, thereby promoting more sustainable farming practices.

Soil Health Management

Soil health is critical to agricultural productivity, and AI applications help farmers monitor and enhance soil conditions effectively. AI-driven systems enable precise management of soil health parameters, allowing farmers to make informed decisions that can improve yield and environmental outcomes. This proactive approach is essential for maintaining soil vitality and supporting long-term agricultural sustainability.

Predictive Analytics

Predictive analytics, powered by AI, is another significant area of application in agriculture. By analyzing historical data and real-time information, farmers can anticipate market trends, optimize crop choices, and make informed decisions that align production with consumer demand. This not only enhances profitability for farmers but also contributes to a more resilient food supply chain.

Legal Challenges

The integration of artificial intelligence (AI) in agriculture presents significant legal challenges, particularly concerning liability, data privacy, intellectual property restrictions and regulatory compliance. As autonomous agricultural machinery becomes more prevalent, the question of liability becomes increasingly complex. Manufacturers bear the responsibility for safety compliance during the design, production, and testing phases of these technologies. However, in the event of an accident, liability may be shared among manufacturers, operators, and software developers, creating complications in legal claims and defenses. The rapid advancement of technology outpaces existing legal frameworks, necessitating continual reassessment of laws to address emerging challenges related to data privacy and cybersecurity risks.

Right to Repair (RTR) Wrinkle

The integration of AI into modern agricultural equipment has led to increased concerns regarding farmers’ ‘right to repair.’ As machinery becomes more reliant on proprietary software and complex electronic systems, farmers face significant obstacles when attempting to diagnose and fix their own equipment. This intellectual property is locked down as manufacturers don’t want customers to handle very complex systems they no nothing about. If they do tinker with these systems, any warranty may be void. This has sparked a nationwide debate, with farmers and advocates pushing for legislation that would require manufacturers to provide access to necessary repair tools, software, and documentation. The core issue revolves around the balance between manufacturers’ intellectual property rights and farmers’ ability to maintain and operate their essential equipment.[xv] While some manufacturers have entered into agreements to provide certain diagnostic tools, many argue that these measures fall short of ensuring true repair independence. Ongoing legislative efforts at both the state and federal levels aim to address these concerns and establish clear guidelines for right to repair in the AI-driven agricultural sector.”

RTR Legislative Efforts on Right to Repair

Various states are considering or have enacted right to repair laws, with a focus on agricultural equipment. Information on state legislation can be found through the National Conference of State Legislatures.[xvi]

Manufacturer Agreements and Diagnostic Tools

Some manufacturers have entered into memorandums of understanding (MOUs) with organizations like the American Farm Bureau Federation to provide diagnostic tools. Investigate Midwest provides information regarding the limitations of the agreements.[xvii]

Liability Considerations

The evolving nature of agricultural technology raises intricate issues regarding liability. Courts and regulatory bodies face difficulties in determining fault in scenarios where autonomous systems operate independently. This uncertainty requires collaboration among legal professionals, technologists, and agricultural stakeholders to devise coherent legal solutions that address these challenges. Current liability frameworks often struggle to delineate responsibilities among the various parties involved, thereby complicating claims related to the use of autonomous agricultural machinery.

Data Privacy and Protection

The adoption of AI in agriculture also intersects with data privacy laws. For instance, several states have enacted consumer data protection laws that require businesses to conduct data protection assessments when their practices present a heightened risk of harm to consumers, most notably California.[xviii] Such assessments are particularly relevant in the context of profiling and automated decision-making, where the potential for unfair treatment or privacy violations is heightened. The Delaware Personal Data Privacy Act and similar legislation mandate that consumers be informed of their rights regarding data processing and allow them to opt-out of certain automated decisions that could have significant effects on their lives.

Regulatory Compliance

As AI technologies continue to evolve, compliance with regulatory frameworks becomes a pressing concern for agricultural stakeholders. New legislation, such as the Tennessee Information Protection Act [xix], emphasizes the need for transparency and accountability in data handling practices. It requires that businesses conduct data protection assessments when processing personal data for profiling, further complicating the legal landscape for agricultural technology users. Moreover, ongoing developments in AI regulation will likely impact how agricultural businesses operate and manage their technological advancements.

Ethical Considerations

The integration of Artificial Intelligence (AI) in agriculture raises significant ethical considerations that must be addressed to ensure responsible development and implementation of technology in the sector. This framework focuses on the ethical legitimacy and accountability of AI-based solutions in farming, emphasizing the importance of trust among stakeholders, particularly farmers.[xx]

Accountability and Legal Responsibility: Contracts Required

A critical ethical question involves determining accountability for errors that may occur during the use of AI technologies. Without clear legal agreements outlining the responsibilities and rights of all parties involved in the development, maintenance, and utilization of these technologies, it becomes challenging to ascertain who is liable for any resulting financial or reputational losses. For instance, John Deere’s practices have raised concerns as their agreements absolve the company from responsibility for damages incurred by farmers due to AI system failures, highlighting the ethical dilemmas surrounding accountability in agricultural technology. John Deere has been sued by the Federal Trade Commission on others in 2025 over this and right to repair causes of action and the contracts are critical pieces of evidence in these cases.[xxi]

Ethical Frameworks and Guidelines

Several ethical AI frameworks and standards exist to guide the development of responsible technologies in agriculture. These include the IEEE Ethics in Action in Autonomous and Intelligent Systems, ISO/IEC JTC 1/SC 42 on Artificial Intelligence, and the European Commission’s Ethics Guidelines for Trustworthy AI. These frameworks encourage the incorporation of ethical considerations at all stages of the technology’s lifecycle, from design to deployment, fostering trust and improving stakeholder relations.[xxii]

Environmental Sustainability

The environmental impact of AI applications in agriculture is another ethical concern. The environmental laws may apply. Technologies such as robots and drones can inadvertently lead to pollution through the misuse of chemicals or improper water management, underscoring the need for responsible AI solutions that prioritize environmental sustainability and regulatory compliance.[xxiii] Algorithms should be designed to minimize environmental harm, aligning technological advancement with ecological stewardship.

Economic Implications

AI’s adoption in agriculture may exacerbate economic inequalities, particularly in leading to certain unemployment. While automation can increase productivity, it may also lead to reduced demand for human labor, threatening livelihoods and potentially worsening poverty in communities reliant on agriculture.[xxiv] Therefore, efforts should be directed towards ensuring economic transition access to AI technologies and addressing the socioeconomic implications that arise from their implementation.

Future Prospects

The future of artificial intelligence (AI) in agriculture appears to be promising, with significant advancements poised to reshape the industry. Key trends indicate that AI technologies will drive innovations such as autonomous farm vehicles, AI-enhanced crop breeding, and the integration of the Internet of Things (IoT) for smarter farming practices.[xxv]

Advanced Technologies in Agriculture

Emerging technologies such as advanced robotics and precision farming are at the forefront of this transformation. AI-driven robots are expected to perform complex agricultural tasks with increased accuracy, significantly boosting productivity.[xxvi] Additionally, AI plays a crucial role in the development of new crop varieties that possess enhanced resistance to pests and diseases, contributing to higher yields and resilience against environmental stressors. Furthermore, the synergy between AI and IoT will facilitate real-time data collection and analysis, allowing farmers to make informed decisions that lead to better crop yields and improved resource management. This interconnected approach will likely revolutionize agricultural practices, making them more responsive to environmental conditions and market demands.

Challenges to Adoption and Legal Frameworks

There is a balance between pre-emptive legal frameworks being adopted for AI in agriculture, and a reactive approach, seen as letting the technology develop without burdensome regulation. Despite the potential benefits, the widespread adoption of AI in agriculture faces several barriers that have legal implications. Key challenges include a lack of technical knowledge among farmers, limited access to necessary equipment and high-quality data, and significant investment costs. In developing countries and economically struggling areas in the United States, these hurdles can impede the integration of advanced technologies, limiting the agricultural sector’s ability to innovate and enhance productivity. Moreover, as AI technologies become more prevalent, there is an increasing need for legal frameworks to address issues such as data privacy, intellectual property rights, and ethical considerations surrounding AI applications in agriculture. [xxvii] Stakeholders must collaborate to navigate these challenges while promoting the adoption of innovative solutions that can lead to sustainable agricultural practices. In the meantime, read and negotiate your contracts.

For assistance on AI and its impact on agriculture and contracts, contact Buckley Law attorney Martin Medeiros at 503-620-8900.

RESOURCES:
[i] https://www.mdpi.com/2071-1050/17/5/2281
[ii] https://techbullion.com/transforming-farming-the-role-of-ai-and-automation-in-modern-agriculture-technology/
[iii] https://medium.com/@ciowomenmagazine/the-impact-of-ai-on-agricultural-labor-e033be5e3112
[iv] https://extension.psu.edu/you-might-already-be-using-artificial-intelligence-without-knowing-it
[v] https://www.fao.org/newsroom/detail/Artificial-Intelligence-best-practices-in-agriculture-can-help-bridge-the-digital-divide-while-tackling-food-insecurity/ar
[vi] https://www.bclplaw.com/en-US/events-insights-news/us-state-by-state-artificial-intelligence-legislation-snapshot.html
[vii] https://www.aimspress.com/article/doi/10.3934/agrfood.2024052
[viii] https://johnbuttery.com/blog/ai-in-agriculture-50-ways-technology-is-transforming-farming
[ix] https://agfundernews.com/guest-article-ai-can-transform-precision-agriculture-but-what-are-the-legal-risks
[x] https://www.agrifarming.in/10-practical-applications-of-ai-in-agriculture-impacts-and-benefits-of-crop-health-and-yield
[xi] https://www.stack-ai.com/articles/how-is-ai-revolutionizing-agriculture-and-farming
[xii] https://intellias.com/artificial-intelligence-in-agriculture/
[xiii] https://aimojo.io/ai-applications-agriculture/
[xiv] https://aaronhall.com/liability-for-autonomous-agricultural-machinery/
[xv] https://www.fb.org/issue/right-to-repair
[xvi] https://www.ncsl.org/technology-and-communication/right-to-repair-2023-legislation
[xvii] https://investigatemidwest.org/2024/04/10/farmers-have-clamored-for-the-right-to-repair-for-years-its-getting-little-traction-in-john-deeres-home-state/
[xviii] https://oag.ca.gov/privacy/ccpa
[xix] https://www.capitol.tn.gov/Bills/113/Bill/HB1181.pdf
[xx] https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2022.884192/full
[xxi] https://www.ftc.gov/news-events/news/press-releases/2025/01/ftc-states-sue-deere-company-protect-farmers-unfair-corporate-tactics-high-repair-costs
[xxii] Id.[
xxiii]Id.,  https://www.emerald.com/insight/content/doi/10.1108/bfj-02-2023-0132/full/html
[xxiv] https://hir.harvard.edu/the-future-of-farming-artificial-intelligence-and-agriculture/
[xxv] https://www.analyticsinsight.net/artificial-intelligence/ai-in-agriculture-precision-farming-and-beyond
[xxvi] https://medium.com/@ciowomenmagazine/case-studies-of-ai-in-agriculture-smart-farming-solutions-5d6104558aaa
[xxvii] https://lawnguilt.com/tech-in-agriculture-legal-challenges-and-innovations/