Can generative AI make farming decisions? Current status and future pathways – A case study in row crop production with ChatGPT

作者：Nipuna Chamara, Yu Pan, Saleh Taghvaeian, Cory Walters, Chris Proctor, Daran R. Rudnick, Daren D. Redfearn, Joe Luck, Yufeng Ge · 发表于：Artificial Intelligence in Agriculture · 年份：2026 · DOI：10.1016/j.aiia.2026.03.008 · 被引用次数：3 · 研究领域：Smart Agriculture and AI、AI in Service Interactions、Artificial Intelligence in Healthcare and Education

The agricultural decision-making process is experience-based, knowledge-dependent, time-sensitive, complex, and driven by historical data. Planting, fertilization, irrigation, and chemigation are key categories in farm decision-making, and currently, there is no one-shot decision-support tool that covers all these activities. Generative Artificial Intelligence (AI) models are more advanced than traditional machine learning and deep learning models. These models have been trained on vast amounts of data from the internet, allowing them to accept unstructured data in various forms and generate human-like text, solutions to problems, and scenario predictions. Given this capability, we became interested in exploring the potential of generative AI in agricultural decision-making. We designed a study to evaluate how well these models can make management decisions in a row crop production environment with humans in the loop. The study began in March 2024 on sprinkler corn plots in North Platte, NE, managed by the TAPS (Testing AG Performance Solutions) program at the University of Nebraska-Lincoln. We evaluated the ChatGPT-4o generative AI model, developed by OpenAI, in terms of its ability to generate decisions for seed selection, cover crop termination, fertigation, irrigation, and chemigation in real time. The model's input included unstructured past management decisions from the TAPS program, 2024 pre-plant soil health lab reports, farm management decision request emails from th...