Integrating UAVs, satellite remote sensing, and machine learning in precision agriculture: pathways to sustainable food production, resource efficiency, and scalable innovation

Yingyig Xing, Xuning Liu, Xiukang Wang · 2026 · Frontiers in Agronomy

Summary. This review examines how UAVs, satellite remote sensing, and machine learning together improve agricultural efficiency and sustainability. These technologies enable real-time crop monitoring, large-scale data analysis, and precise decision-making for irrigation and nutrient management. Case studies show the integration reduces irrigation costs by 20–25% and nitrogen use by 31 kg/ha while maintaining yields, and achieves over 95% accuracy in disease detection. Success requires supportive policies, affordable technology access for smallholder farmers, and interdisciplinary collaboration.

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@article{xing-2026-integrating-uavs-satellite-remote-sensing,
  title   = {Integrating UAVs, satellite remote sensing, and machine learning in precision agriculture: pathways to sustainable food production, resource efficiency, and scalable innovation},
  author  = {Yingyig Xing and Xuning Liu and Xiukang Wang},
  journal = {Frontiers in Agronomy},
  year    = {2026},
  doi     = {10.3389/fagro.2025.1670380},
  url     = {https://doi.org/10.3389/fagro.2025.1670380}
}

TY  - JOUR
TI  - Integrating UAVs, satellite remote sensing, and machine learning in precision agriculture: pathways to sustainable food production, resource efficiency, and scalable innovation
AU  - Yingyig Xing
AU  - Xuning Liu
AU  - Xiukang Wang
JO  - Frontiers in Agronomy
PY  - 2026
DO  - 10.3389/fagro.2025.1670380
UR  - https://doi.org/10.3389/fagro.2025.1670380
ER  - 

Details

DOI

10.3389/fagro.2025.1670380

Countries

China

Regions

Asia

Categories

agtech, food-systems, policy, general-innovation

Added

2026-10-01