High-throughput wheat seedling phenotyping via UAV-based semantic segmentation and ground sample distance driven pixel-to-area mapping

Abstract

• Combines semantic segmentation with GSD-based spatial metrics for precise seedling evaluation. • Wheat Seedling Former model, with preprocessing, exceeds current methods in identifying seedling structures. • Utilizes UAV RGB and multispectral data for non-destructive, high-throughput phenotyping. • Facilitates rapid assessment of seedling vigor and canopy plasticity for large-scale screening of stress-resilient wheat cultivars. Traditional methods for estimating wheat seedling area, such as manual grid sampling or ground-based sensors, suffer from low precision, labour intensity, and limited scalability under complex field conditions. To address these challenges, this study introduces a pixel-to-area phenotyping framework that integrates Wheat Seedling Former semantic segmentation with Ground Sample Distance (GSD)-based spatial conversion to achieve high-throughput quantification of wheat seedling coverage and growth vigour. The framework employs a three-step preprocessing pipeline, linear regression-based colour calibration, super-green (ExG) segmentation, and modified anisotropic diffusion filtering, to enhance image quality and suppress noise. The Wheat Seedling Former network incorporates a spatial-channel dual attention module to mitigate background interference and a cross-layer feature pyramid architecture to capture fine-scale morphological traits (e.g., leaf edges, tiller distribution). By aligning RGB and multispectral imagery via geometric correction (holography transformation) and spectral correction (soil-reflection suppression), the framework quantifies six phenotypic indices: seedling coverage area, canopy compactness, NDVI, NDRE, chlorophyll index, and foliage projection coverage. Applied to 160 field plots, the model achieved a Pearson correlation coefficient of 0.942 with ground-truth measurements, demonstrating high accuracy. GSD-based spatial conversion reduced scaling errors to < 3 %, enabling precise area estimation (±0.5 m 2 ) even on uneven terrain. Phenotypic analysis stratified plots into three vigor classes: 35 high-performing (≥90 % canopy closure), 83 medium (60–90 %), and 42 low (<60 %), with high-performing genotypes showing 28 % higher drought tolerance. A software tool (Seedling Phenotype Extractor) automates image annotation, phenotypic calculations, and genotype ranking, reducing phenotyping time by 65 %. This pipeline bridges computational precision and field-scale breeding applications, offering a scalable tool for accelerating the discovery of stress-resilient wheat cultivars through rapid, non-destructive assessment of early-season canopy plasticity.

Plant phenotyping relevance

UAV画像、意味分割、GSD変換を統合し、植物の被覆面積・樹冠構造・スペクトル指標などを高スループットで抽出する手法の開発と検証が中心である。

Code and data availability

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