The BELT and phenoSEED platforms: shape andcolour phenotyping of seed samples

Abstract

Background Seed analysis is currently a bottleneck in phenotypic analysis of seeds. Measurements are slow and imprecise with potential for bias to be introduced when gathered manually. New acquisition tools were requested to improve phenotyping efficacy with an emphasis on obtaining colour information. Results A portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis. Lentil ( Lens culinaris L.) phenotyping was used as the primary test case. Seeds were loaded into the system and all seeds in a sample were automatically and individually imaged to acquire top and side views as they passed through an imaging chamber. A Python analysis script applied a colour calibration and extracted quantifiable traits of seed colour, size and shape. Extraction of lentil seed coat patterning was implemented to further describe the seed coat. The use of this device was forecasted to eliminate operator biases, increase the rate of acquisition of traits, and capture qualitative information about traits that have been historically analyzed by eye. Conclusions Increased precision and higher rates of data acquisition compared to traditional techniques will help breeders to develop more productive cultivars. The system presented is available as an open-source project for academic and non-commercial use.

Plant phenotyping relevance

種子の色・サイズ・形状・種皮模様を自動取得・抽出する撮像システムと解析ソフトウェアの開発が中心であり、植物フェノタイピング手法に該当する。

Code and data availability

The paper's phenoSEED image-analysis script (used for seed shape, size, colour, and clustering phenotyping) is explicitly stated to be publicly available on the authors' GitLab repository. The BELT image datasets themselves are only available on request, so they do not qualify as public assets.