A new computer vision workflow to assess yield quality traits in bush bean (Phaseolus vulgaris L.)

Abstract

Quality assessments of horticultural products are still often carried out manually in breeding contexts, although computer vision systems have been reported to be able to overcome the limitations of manual assessments, e.g. in automated food processing. Here, a new computer vision workflow for quality trait assessment of bush bean pods (Phaseolus vulgaris) is introduced to replace physical measurements and visual scorings of expert breeders, while increasing consistency, accuracy, and objectivity of the measurements. A closed imaging box was used to take images of bean pods from 40 different varieties to develop and validate computer vision workflows to assess breeding relevant shape and color traits of bean pods. For the detection of beaks and peduncles via a neural network approach (Mask R-CNN) accuracies of 95.5% were reached. Computer vision estimations and manual reference measurements of length and caliber were highly correlated (R=0.99). Also, curvature and brightness of green bean pods well- correlated with visual scorings of expert breeders (R=0.81, R=-0.87). A Random Forest Classifier was trained to distinguish yellow and extremely rare bicolored pods and a cross validation accuracy of 83 ±7% was reached. An additional backlight LED panel enabled non-destructive analysis of seed formation inside the pod and promising results were achieved using a Faster R-CNN model. This new computer vision workflow provides the opportunity to replace well-established manual workflows for quality trait assessment of bush bean pods as it is more objective, reliable, and considerably faster.

Keywords

• Horticultural crop grading
• Plant breeding
• RGB imaging
• Mask R-CNN
• Random forest classifier
• High-throughput