High-Throughput Phenotyping of Seed Quality Traits Using Imaging and Deep Learning in Dry Pea

Abstract

Abstract Seed traits, such as seed color and seed size, directly impact seed quality, affecting the marketability and value of dry peas [1]. Assessing seed quality is integral to a plant breeding programs to ensure optimal seed standards. This research introduced a phenotyping tool to assess seed quality traits specifically tailored for pulse crops, which integrates image processing with cutting-edge deep learning models. The proposed method is designed for automation, seamlessly processing a sequence of images while minimizing human intervention. The pipeline standardized red-green-blue (RGB) images captured from a color light box and used deep learning models to segment and detect seed features. Our method extracted up to 86 distinct seed characteristics, ranging from basic size metrics to intricate texture details and color nuances. Compared to traditional methods, our pipeline demonstrated a 95 percent similarity in seed quality assessment and increased time efficiency (from 2 weeks to 30 minutes for processing time). Specifically, we observed an improvement in the accuracy of seed trait identification by simply using an RGB value instead of a categorical, non-standard description, which allowed for an increase in the range of detectable seed quality characteristics. By integrating conventional image processing techniques with foundational deep learning models, this approach emerges as a pivotal instrument in pulse breeding programs, guaranteeing the maintenance of superior seed quality standards.

Plant phenotyping relevance

画像処理と深層学習を統合した種子形質抽出ツールの開発が中心であり、種子のサイズ・色・質感などの植物表現型を自動測定している。

Code and data availability

The supplied blocks describe a dry pea seed phenotyping pipeline (SAM annotation, YOLOv8 detection, 86 extracted traits) but contain no public dataset, image, code, or model deposit with an authors' URL. Supplementary materials (Appendix 1 light box instructions, Appendix 2 regression summaries) are referenced but no公开

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