Characterization of a Collection of Colored Lentil Genetic Resources Using a Novel Computer Vision Approach.

Abstract

The lentil ( Lens culinaris Medik.) is one of the major pulse crops cultivated worldwide. However, in the last decades, lentil cultivation has decreased in many areas surrounding Mediterranean countries due to low yields, new lifestyles, and changed eating habits. Thus, many landraces and local varieties have disappeared, while local farmers are the only custodians of the treasure of lentil genetic resources. Recently, the lentil has been rediscovered to meet the needs of more sustainable agriculture and food systems. Here, we proposed an image analysis approach that, besides being a rapid and non-destructive method, can characterize seed size grading and seed coat morphology. The results indicated that image analysis can give much more detailed and precise descriptions of grain size and shape characteristics than can be practically achieved by manual quality assessment. Lentil size measurements combined with seed coat descriptors and the color attributes of the grains allowed us to develop an algorithm that was able to identify 64 red lentil genotypes collected at ICARDA with an accuracy approaching 98% for seed size grading and close to 93% for the classification of seed coat morphology.

Plant phenotyping relevance

画像解析を用いてレンズマメ種子のサイズ、形状、種皮形態、色を非破壊測定・分類する手法を開発し、精度も評価しているため、植物表現型取得が中心です。

Code and data availability

The paper's lentil seed image dataset (64 images of ICARDA genotypes used for the computer vision phenotyping pipeline) is explicitly stated to be publicly available on the authors' GitHub repository. The Data Availability Statement only offers other data upon request, but the image dataset itself has a public URL.