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This review aims to explore how artificial intelligence can support smallholder farmers and identify gaps in farmer engagement with AI technologies.

July 16, 2026AgricultureOpen Access

Technology-Driven or Farmer-Centred? A Systematic Review of Artificial Intelligence Research for Smallholder Agriculture

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This review aims to explore how artificial intelligence can support smallholder farmers and identify gaps in farmer engagement with AI technologies.
Conducted a systematic literature review following PRISMA 2020 guidelines.
Analyzed 182 studies using databases such as Web of Science, Scopus, and EBSCOhost.
Examined applications and impacts of AI technologies in small-scale agriculture.
A rapid increase in AI-associated publications was noted post-2020, mainly from Africa and Asia.
Technical applications identified included plant disease detection, crop yield prediction, and environmental monitoring, primarily using machine learning and deep learning.
Only a few studies addressed farmer interaction with AI, highlighting a gap towards a farmer-centred approach.

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Systematic review examines the application and challenges of AI in smallholder agriculture, suggesting a need for farmer involvement.

Coka et al. (2026) studied this question.

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