Use of Drones In Crop Health Monitoring: A Comprehensive Review (2014–2025)

Abstract

Drones (unmanned aerial vehicles, UAVs) have rapidly transitioned from experimental tools to dependable, field-scale systems for crop health monitoring. By delivering on-demand, centimeter-level imagery and thermal/structural measurements, UAVs enable early diagnosis of nutrient limitations, water stress, pest and disease outbreaks, and stand establishment issues. This review synthesizes the state of the art in UAV platforms and sensors (RGB, multispectral, hyperspectral, thermal, LiDAR), radiometric and geometric workflows, vegetation indices and biophysical proxies, and analytics using machine learning and deep learning. Practical agronomic applications—nutrient management, irrigation scheduling, weed mapping, variable-rate prescriptions, lodging assessment, and yield forecasting—are evaluated alongside economics, environmental benefits, and operational constraints. We also discuss policy and capacity considerations for large-scale deployment, with emphasis on emerging markets. Finally, we outline future directions in multimodal sensor fusion, 3D/temporal retrievals, edge autonomy, and foundation AI models for robust, field-ready decision support.

Code and data availability

This is a narrative review of UAV crop health monitoring with no original phenotyping measurements, datasets, images, code, models, or supplements. All cited works are prior publications, and no data or code availability statements or public URLs appear in the supplied blocks.

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