Semantic Segmentation of Packaged and Unpackaged Fresh-Cut Apples Using Deep Learning

Computer vision systems are often used in industrial quality control to offer fast, objective, non-destructive, and contactless evaluation of fruit. The senescence of fresh-cut apples is strongly related to the browning of the pulp rather than to the properties of the peel. This work addresses the identification and selection of pulp inside images of fresh-cut apples, both packaged and unpackaged; this is a critical step towards a computer vision system that is able to evaluate their quality and internal properties. A DeepLabV3+-based convolutional neural network model (CNN) has been developed for this semantic segmentation task. It has proved to be robust with respect to the similarity of colours between the peel and pulp. Its ability to separate the pulp from the peel and background has been verified on four varieties of apples: Granny Smith (greenish peel), Golden (yellowish peel), Fuji, and Pink Lady (reddish peel). The semantic segmentation achieved an accuracy greater than 99% on all these varieties. The developed approach was able to isolate regions significantly affected by the browning process on both packaged and unpackaged pieces: on these areas, the colour analysis will be studied to evaluate internal quality and senescence of packaged and unpackaged products.

Automated summary

Computer vision systems offer fast, objective, non-destructive, and contactless evaluation of fruit, and this work focuses on the identification and selection of pulp inside images of fresh-cut apples. A DeepLabV3+-based CNN model has been developed for semantic segmentation, successfully separating the pulp from the peel and achieving an accuracy greater than 99% on different varieties of apples. This approach can isolate regions affected by browning, allowing for color analysis to evaluate the internal quality and senescence of packaged and unpackaged products.