Drone-Based Phenotyping and its Utilization in Crop Improvement: A Review

Abstract

Drone-based phenotyping using unmanned aerial vehicles (UAVs) has emerged as a revolutionary approach for high-throughput, precise, and scalable measurement of plant traits critical to crop improvement. This technology integrates advanced imaging sensors—including RGB, multispectral, hyperspectral, and thermal cameras—with sophisticated image processing and artificial intelligence algorithms to non-destructively capture key phenotypic data such as plant height, biomass, canopy temperature, maturity timing, and disease symptoms under natural field conditions. Compared with traditional manual phenotyping and satellite-based remote sensing, UAV phenotyping offers superior spatial and temporal resolution, enabling dynamic monitoring of complex traits such as drought tolerance and disease resistance. Applications span early stress detection, quantitative trait assessment, yield prediction, and accelerating breeding cycles by facilitating objective, rapid selection of superior genotypes across multiple crop species. Despite its transformative potential, challenges remain in standardizing protocols, managing large-scale complex datasets, integrating phenotypic with genomic and environmental data, and providing training resources for widespread adoption. Ongoing advancements in sensor technology, data analytics, open-source tools, and capacity building are poised to cement drone-based phenotyping as a cornerstone technology for sustainable, climate-resilient crop breeding and global food security.

Plant phenotyping relevance

UAV画像・センサーによる植物形質計測を中心に扱う明示的なフェノタイピングレビューであり、手法の応用、技術、課題を総合的に論じている。

Code and data availability

This is a review article with no original phenotyping measurements, datasets, images, or analysis code of its own. All referenced resources (e.g., MatchPlant, EIB SOPs, cited studies) are prior work or generic tools, not paper-specific assets of this review.

No evidence-backed public reproduction asset is currently recorded.