This rapid review examines the application of artificial intelligence (AI) in food and agriculture systems, with a particular focus on low- and middle-income countries (LMICs). It synthesises evidence from 51 studies published between 2019 and 2024, including quantitative, qualitative, and mixed-methods research.

The study follows an adapted systematic review methodology based on Cochrane and Campbell frameworks, combining structured search strategies, critical appraisal tools, and mixed-method approaches (PICO and SPIDER frameworks). The review analyses how AI is defined and implemented across agricultural value chains, including applications such as machine learning, robotics, remote sensing, and conversational tools.

Findings show that while AI demonstrates strong potential, particularly in crop productivity, disease detection and resource efficiency, most evidence is based on simulations or predictive modelling rather than real-world field outcomes. Significant gaps remain in assessing the impacts on effectiveness, inclusivity and equity, particularly in smallholder farming and low-income settings.

Overall, the publication highlights both the promise and limitations of current AI deployment in agriculture, emphasising the need for more rigorous, inclusive, and context-sensitive research to ensure equitable and scalable benefits.