Leveraging Machine Learning for Precision and Sustainable Agriculture: Integrative Approaches for Crop and Soil Management

Precision and sustainable agriculture are critical in addressing global food security and environmental challenges, where leveraging machine learning (ML) offers significant potential for innovation. This review synthesizes recent advances in ML techniques including supervised, unsupervised, and advanced models such as deep reinforcement learning, applied to enhance crop and soil management practices. Key applications highlighted encompass early crop disease detection using UAV multispectral imaging, yield prediction integrating satellite and climate data, cultivar classification, and accurate soil property estimation employing VIS-NIR spectroscopy and hydrologic data. Additionally, the integration of IoT with AI facilitates precision irrigation and real-time analytics in agricultural supply chains, enhancing sustainability. Challenges related to data quality, model scalability, multi-source data integration, and automation are critically discussed, alongside emerging ML paradigms poised to transform smart agriculture. This comprehensive overview underscores the transformative role of ML in advancing precision and sustainability in agriculture, outlining future research avenues to overcome existing limitations.