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Application of Unmanned Aircraft Systems (UASs) for Disease Assessment and High Throughput Field Phenotyping of Plant Breeding Trials

Research Square · 29 Dec 2025 · 10.21203/rs.3.rs-8436422/v1

Abstract

Abstract Conventional plant phenotyping relies on visual scoring and manual measurements, which are labor-intensive, time-consuming, and prone to human error. To address these limitations, Unmanned Aircraft Systems (UASs) are increasingly being applied in breeding trials to capture various phenotypic traits. High Throughput Phenotyping (HTP) offers enhanced speed, accuracy, and efficiency, while potentially reducing costs in plant breeding programs. This study explores UAS-based phenotyping in wheat breeding trials with the aim to integrate HTP platforms across breeding pipelines. UAS images were acquired using a Parrot Bluegrass drone equipped with a sequoia multispectral sensor, processed via Agisoft Metashape (open-source) and Pix4D mapper (licensed), and analyzed using PlotPhenix (licensed) for Vegetation Indices (VIs) and plot segmentation. Comparisons between UAS-based and ground-based measurements revealed that grain yield is significantly negatively correlated (r = -0.74) with yellow rust disease severity. Multispectral-derived indices, particularly **Red, Red Edge, and NIR bands, showed positive correlations with grain yield (ranging from 0.22 to 0.23), though RGB-generated indices exhibited stronger correlations. The findings confirm that UAS-generated indices effectively assess yellow rust disease severity and predict grain yield. UAS-based phenotyping enhances efficiency and accuracy in trait collection and disease assessment, facilitating the development of improved wheat varieties and promoting the integration of UAS technologies into breeding programs.

Plant phenotyping relevance

UAS・マルチスペクトル画像を用いた作物形質取得、区画分割、疾病重症度評価、収量予測を中心に扱う高スループット表現型解析研究であり、方法の適用とプラットフォーム統合が中心です。

titleApplication of Unmanned Aircraft Systems (UASs) for Disease Assessment and High Throughput Field Phenotyping of Plant Breeding Trials
abstractThis study explores UAS-based phenotyping in wheat breeding trials with the aim to integrate HTP platforms across breeding pipelines.
abstractUAS images were acquired using a Parrot Bluegrass drone equipped with a sequoia multispectral sensor, processed via Agisoft Metashape (open-source) and Pix4D mapper (licensed), and analyzed using PlotPhenix (licensed) for Vegetation Indices (VIs) and plot segmentation.
abstractThe findings confirm that UAS-generated indices effectively assess yellow rust disease severity and predict grain yield.

Code and data availability

The article describes UAS-based wheat phenotyping (multispectral imagery, disease scoring, vegetation indices) but contains no public data deposit, no author analysis code/scripts, no trained models, and no supplementary data availability statement. Software mentioned (Pix4D, Agisoft Metashape, Plotphenix) are third-或-

No evidence-backed public reproduction asset is currently recorded.

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