Development of automatic detection model for stem-end rots of ‘Hass’ avocado fruit using X-ray imaging and image processing

Abstract

Postharvest qualitative avocado losses attributable to stem-end rot are serious problems in various main production areas. Postharvest inspection of avocadoes is still commonly performed by hand; this process depends upon only slight differences in fruit appearance or firmness, and it can lead to misclassification. The development of a non-destructive stem-end rot detection technique would help improve objective inspection and reduce misclassification. Therefore, this study proposes an X-ray machine vision approach to automatically detect stem-end rot in avocados. Four-hundred-and-twenty-two X-ray images of fully ripened ‘Hass’ avocadoes were obtained to develop and validate an automatic classification model. The stem-end rot features visible in the X-ray images were extracted using two image processing operations: image segmentation and fruit-outline evaluation. Then, an extremely randomized trees model classified rot development into two classes (< 20% and ≥ 20%) using two features extracted from the X-ray image. The overall classification accuracy was 0.89 with an area under the curve of 0.93 and a recall of 0.89, which suggests the possibility of performing stem-end rot inspections via X-ray imaging and image processing.