Characterization of a collection of colored lentil genetic resources using a novel computer vision approach

Lentil (Lens culinaris Medik.) is one of the major pulse crops cultivated worldwide. However, in the last decades lentil cultivation decreased in many areas surrounding the Mediterranean Countries due to low yields, new lifestyles and changed eating habits. Thus, many landraces and local varieties disappeared, while local farmers are the only custodians of the treasure of lentil genetic resources. Recently, lentil has been rediscovered to meet the needs of a more sustainable agriculture and food systems. Here we propose an image analysis approach that besides being rapid and non-destructive method can characterize seed size grading and seed coat morphology. 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 colour attributes of the grains allowed us to develop an algorithm able to identify 64 red lentil genotypes collected at ICARDA with an accuracy approaching 98% for seed size grading and close to 93% for classification of seed coat morphology respectively.