The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples.

Abstract

Abstract Background Quantitative and qualitative assessment of visual and morphological traits of seed is slow and imprecise with potential for bias to be introduced when gathered with handheld tools. Colour, size and shape traits can be acquired from properly calibrated seed images. New automated tools were requested to improve data acquisition efficacy with an emphasis on developing research workflows. 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 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 to extract larger datasets and explore more research questions. 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・種皮模様を画像から自動取得・定量化する撮像システムと解析ソフトウェアを開発しており、植物フェノタイピング手法が研究の中心である。

Code and data availability

The paper explicitly states that the phenoSEED analysis script is publicly available on GitLab and that the BELT-captured seed image datasets are available on the first author's Figshare page. Both are paper-specific, public, and actionable.