Integrating UAVs, satellite remote sensing, and machine learning in precision agriculture: pathways to sustainable food production, resource efficiency, and scalable innovation - Xing, Yingyig; Liu, Xuning; Wang, Xiukang (Frontiers in Agronomy, 2026)
Precision agriculture has emerged as a pivotal approach to achieving sustainable food production by integrating advanced technologies such as Unmanned Aerial Vehicles (UAVs), satellite remote sensing, and machine learning. This review examines the synergistic application of these technologies in enhancing agricultural efficiency, resource optimization, and environmental sustainability. UAVs enable high-resolution, real-time monitoring of crop health, soil conditions, and pest infestations, while satellite remote sensing provides scalable, large-scale agricultural data for comprehensive landscape analysis. Machine learning algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs) and Random Forests (RFs), process complex datasets to deliver actionable insights for precision decision-making, such as yield prediction, nutrient management, and irrigation optimization. Case studies demonstrate that integrating UAV and satellite data with machine learning improves crop yield prediction accuracy and resource use efficiency, reducing irrigation costs by 20–25% and nitrogen application by up to 31 kg ha−1, without compromising productivity. AI-driven disease detection systems have demonstrated high efficacy, with certain models achieving accuracy exceeding 95% in identifying diseases such as Botrytis cinerea in tomatoes, powdery mildew in wheat, and downy mildew in grapes. However, challenges persist, including data processing complexities, high computational demands, and the need for cost-effective, scalable solutions. The findings underscore the transformative potential of these technologies in advancing sustainable agriculture, while emphasizing the necessity for interdisciplinary collaboration, supportive policies such as subsidies for precision agriculture equipment, streamlined regulations for UAV operations, and open data initiatives for satellite imagery, as well as improved accessibility to key technologies including high-resolution multispectral sensors, cloud computing infrastructure, and scalable machine learning platforms for smallholder farmers. This review provides a roadmap for future research and policy development aimed at optimizing food production systems in the face of climate change and growing population demands.