By: Esha Kher

Precision agriculture is revolutionizing modern farming by integrating advanced technologies, particularly artificial intelligence. AI has improved farming efficiency by utilizing data-driven decision-making, autonomous machinery, and predictive analytics to optimize resource use and enhance climate resilience. This transformation is driven by three key technologies: machine learning, which provides actionable insights; robotics, which automates tasks like harvesting; and Internet of Things devices, such as sensors and drones that enable real-time crop monitoring. These advancements allow farmers to make informed choices about irrigation, fertilization, and pest control, thus boosting efficiency, reducing resource waste, and enhancing climate resilience.

As AI-driven agriculture expands—with the market projected to grow from $1.7 billion in 2023 to $4.7 billion by 2028—the vast collection and processing of agricultural data raises significant concerns about data privacy. Addressing these legal challenges is essential to ensuring that precision agriculture continues to advance while also maintaining responsible data governance and ethical practices.

Data privacy risks in precision agriculture

Data ownership in precision agriculture presents a complex legal challenge, as multiple stakeholders, including agricultural technology providers (ATPs), farmer cooperatives, policymakers, and private-sector entities, are involved. The absence of a universal legal framework defining data ownership in agriculture creates ambiguities around rights related to access, modification, and distribution. 

Many precision farming solutions rely on cloud-based platforms, data analytics firms, and equipment manufacturers that collect and process agricultural data. Studies indicate that many agricultural technology contracts grant extensive control over farm data to technology providers, often without farmers fully understanding the implications. In other cases farmers unknowingly sign away their rights through service agreements that contain complex or vague terms. 

Corporations often employ restrictive data practices to exert control over farmers, limiting their independence and forcing them into costly, unfair dependencies. A recent investigation by the Federal Trade Commission into John Deere’s data practices examined the company’s “right to repair” policies, specifically whether Deere’s restrictions on accessing repair information and software prevent farmers from fixing their own machinery, thereby forcing them to rely on authorized dealerships for repairs. Some Agricultural Tech Providers further restrict farmers’ ability to switch between digital service providers, limiting the farmers’ right to data portability and impeding farmers’ capacity to analyze or use their data elsewhere.

Some companies use farmer data to manipulate prices and profit from this manipulation without compensating the farmers. For example, in the U.S. Corn Belt, thousands of farmers transmit real-time crop yield data from harvesting equipment to a data repository. Despite agreements to the contrary, the repository misappropriated this data by selling early yield estimates to commodity traders.

Additionally, AI-driven risk assessments based on biased or unrepresentative datasets could lead to higher insurance premiums for certain farmers. Similarly, data-driven price discrimination could occur when suppliers use collected data to set different prices based on farm size or productivity potential.

How existing data privacy laws apply to and manage precision agriculture 

Existing data privacy laws play an important role in managing precision agriculture by establishing frameworks for data ownership, access, and security. However, these laws remain insufficient in addressing the sector’s complexities. 

For example, the General Data Protection Regulation (GDPR) in the EU applies when farm data includes personally identifiable information (PII). However, most agricultural data is classified as non-personal and falls outside the regulation’s scope. The GDPR also includes provisions for automated decision-making, requiring transparency and accountability in AI-driven analytics. Though at least 20 states in the U.S. have introduced comprehensive data privacy laws, data collected through precision farming may not necessarily be covered under these regulations. 

In response to regulatory gaps, industry-led initiatives have attempted to provide guidance. The American Farm Bureau Federation’s “Privacy and Security Principles” for Farm Data provide voluntary guidelines to farmer ownership of agricultural data, transparency in data collection, robust security measures by agribusinesses, and clear disclosure in data-sharing agreements. Additionally, the Ag Data Transparency Evaluator is another voluntary assessment designed to help U.S. farmers understand how their data will be used when adopting precision agriculture technologies. The responses that data collectors enter into the evaluator are then reviewed by an independent third-party administrator, and companies that meet the criteria receive the Ag Data Transparent Seal, signaling their compliance with the American Farm Bureau’s Data Principles.

While these voluntary frameworks promote transparency, they lack enforcement mechanisms. Until formal legal frameworks catch up with agricultural technology, the best practice for farmers remains to negotiate well-crafted contracts specifying data control, portability, sharing permissions, anonymization, deletion policies, consent requirements, policy change notifications, data usage restrictions, modification rights, and security measures.

In addition to these contractual safeguards, it is recommended that farmers enhance their digital literacy to better protect the sensitive data collected, stored, and used by emerging technologies. For example, organizations like COPA-COGECA in the EU offer training that helps farmers understand their rights and learn advanced techniques for data integration and analysis, ultimately enabling them to retain greater control over their data.

Conclusion

AI-driven precision agriculture holds immense potential to enhance productivity, sustainability, and efficiency in farming. However, the rapid expansion of AI in this sector also brings significant data privacy risks. Current data privacy laws, such as GDPR and CCPA, provide some level of protection but fail to fully address the complexities of farm data management, leaving farmers vulnerable to unfair data-sharing agreements and monopolistic practices. While voluntary industry initiatives like the Ag Data Transparent Certification promote transparency, stronger legal frameworks are needed to ensure fair data-sharing practices. As AI continues to transform agriculture, policymakers, industry leaders, and farmers must work together to create a regulatory environment that protects farmer autonomy while fostering innovation and technological progress. 

#WJLTA #agriculturaltech #dataprivacy #AI #precisionagriculture