Machine Learning in Sustainable Agriculture: Systematic Review and Research Perspectives

Machine Learning in Sustainable Agriculture: Systematic Review and Research Perspectives

14678888

Agriculture
11.2.25

English

38 pp.

631 Bo13

Machine learning (ML) has transformed agricultural resource management by enabling precise data-driven predictions, boosting productivity, profitability, and sustainability. Despite progress, challenges remain in handling large datasets, infrastructure, data integration, and adapting models to local contexts. A systematic review (2007–2025) using PRISMA and bibliometric analysis identified 124 relevant studies, revealing rapid growth in ML research in sustainable agriculture—especially from 2022 to 2024. Key trends include integrating IoT, remote sensing, and smart farming for better decision-making, soil monitoring, and understanding climate-soil-crop interactions. The study emphasizes the need for deeper exploration of advanced ML techniques like deep learning to expand their impact in sustainable agriculture.

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