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Rapid advances and diffusion of artificial intelligence (AI) technologies have the potential to transform agriculture globally by improving measurement, prediction, and site-specific management on the farm, enabling autonomous equipment that is trained to mimic human behavior and developing recommendation systems designed to autonomously achieve various tasks. Here, we discuss the applications of AI-enabled technologies in agriculture, including those that are capable of on-farm reinforcement learning and key attributes that distinguish them from precision technologies currently available. We then describe various ways through which AI-driven technologies are likely to change the decision space for farmers and require changes to the theoretical and empirical economic models that seek to understand the incentives for their adoption. We conclude with a discussion of areas for future research on the economic, environmental, and equity implications of AI-enabled technology adoption for the agricultural sector.
Khanna et al. (Mon,) studied this question.