Abstract
Artificial intelligence (AI) and precision agriculture technologies are increasingly promoted as tools for addressing food insecurity and improving rural livelihoods in developing countries, but the evidence on their developmental outcomes and governance implications is fragmented across disciplines. This study conducted a PRISMA 2020-guided systematic review of peer-reviewed journal articles and scholarly book chapters published between 1 January 2012 and 15 January 2026 and retrieved from Scopus, Web of Science Core Collection, and the Directory of Open Access Journals (DOAJ). Of 412 records identified, 52 studies met the inclusion criteria. Empirical studies were appraised at the study level with the Mixed Methods Appraisal Tool (MMAT 2018), while reviews and conceptual papers were used for context. Studies were coded against an a priori framework of four governance mechanisms (targeting and allocation; delivery and service provision; monitoring and evaluation; accountability and participation) and synthesised thematically across six domains. The strongest evidence concerns the technical performance of AI models for crop and food-insecurity prediction and for pest and disease detection. Evidence that these tools improve farm incomes, food security or livelihood resilience in practice is limited: few studies measured livelihood or food-security outcomes, were longitudinal, or disaggregated results by gender or farm size. Recurrent constraints include infrastructure deficits, low digital literacy, unrepresentative training data, weak institutional capacity and regulatory gaps. The study contributes a framework that links AI applications to food-security and livelihood outcomes through governance mechanisms and identifies where evidence is strong, emerging or absent. Potential benefits should be regarded as plausible but not yet demonstrated at scale. Priorities include impact evaluations with livelihood endpoints, gender-disaggregated and distributional analysis, and comparative studies of the institutional conditions under which AI-enabled agriculture benefits smallholders.
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APA 7th edition
In-text citation: (Lasisi, 2026)
Reference: Lasisi, M. I. (2026). AI and precision agriculture for food security and rural livelihoods in developing countries: A systematic review.
Asia Pacific Journal of Education and Society, 14(2), Article 13.
https://doi.org/10.20897/apjes/19942
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Lasisi MI. AI and precision agriculture for food security and rural livelihoods in developing countries: A systematic review.
Asia Pacific Journal of Education and Society. 2026;14(2), 13.
https://doi.org/10.20897/apjes/19942
Chicago
In-text citation: (Lasisi, 2026)
Reference: Lasisi, Modinat Iyadunni. "AI and precision agriculture for food security and rural livelihoods in developing countries: A systematic review".
Asia Pacific Journal of Education and Society 2026 14 no. 2 (2026): 13.
https://doi.org/10.20897/apjes/19942
Harvard
In-text citation: (Lasisi, 2026)
Reference: Lasisi, M. I. (2026). AI and precision agriculture for food security and rural livelihoods in developing countries: A systematic review.
Asia Pacific Journal of Education and Society, 14(2), 13.
https://doi.org/10.20897/apjes/19942
MLA
In-text citation: (Lasisi, 2026)
Reference: Lasisi, Modinat Iyadunni "AI and precision agriculture for food security and rural livelihoods in developing countries: A systematic review".
Asia Pacific Journal of Education and Society, vol. 14, no. 2, 2026, 13.
https://doi.org/10.20897/apjes/19942
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Lasisi MI. AI and precision agriculture for food security and rural livelihoods in developing countries: A systematic review. Asia Pacific Journal of Education and Society. 2026;14(2):13.
https://doi.org/10.20897/apjes/19942