A review by Mostafa Farajpour, published in Discover Artificial Intelligence in 2026, surveys how artificial intelligence is being applied in agriculture to support crop production, sustainability and climate mitigation. It covers precision irrigation, nutrient and soil management, yield prediction, pest and disease monitoring, computer vision, autonomous agricultural robotics, and decision-support systems based on large language models (LLMs).

The review assesses deployment conditions as well as technical advances. It says agricultural AI depends on model generalizability, explainability, economic feasibility, interoperability and integration with existing agronomic knowledge, not predictive accuracy alone. It identifies Vision Transformers, foundation models, Digital Twins, multimodal AI, Retrieval-Augmented Generation, and federated and continual learning as directions for addressing data scarcity, environmental variability and the difficulty of deploying systems at scale.

Responsible adoption, the review says, requires transparent governance for data ownership, privacy, algorithmic fairness, cybersecurity and equitable access, particularly for smallholder farms. Despite reports of strong model performance, adoption remains constrained by limited cross-region validation, fragmented datasets, infrastructure needs and insufficient real-world deployment. The review calls for collaboration across AI, sensing, robotics, crop science and agricultural engineering to build robust, trustworthy decision-support systems centered on farmers.

A review by Mostafa Farajpour, published in Discover Artificial Intelligence in 2026, surveys how artificial intelligence is being applied in agriculture to support crop production, sustainability and climate mitigation. It covers precision irrigation, nutrient and soil management, yield prediction, pest and disease monitoring, computer vision, autonomous agricultural robotics, and decision-support systems based on large language models (LLMs).

The review assesses deployment conditions as well as technical advances. It says agricultural AI depends on model generalizability, explainability, economic feasibility, interoperability and integration with existing agronomic knowledge, not predictive accuracy alone. It identifies Vision Transformers, foundation models, Digital Twins, multimodal AI, Retrieval-Augmented Generation, and federated and continual learning as directions for addressing data scarcity, environmental variability and the difficulty of deploying systems at scale.

Responsible adoption, the review says, requires transparent governance for data ownership, privacy, algorithmic fairness, cybersecurity and equitable access, particularly for smallholder farms. Despite reports of strong model performance, adoption remains constrained by limited cross-region validation, fragmented datasets, infrastructure needs and insufficient real-world deployment. The review calls for collaboration across AI, sensing, robotics, crop science and agricultural engineering to build robust, trustworthy decision-support systems centered on farmers.