Orange Quality Grading with Deep Learning

Abstract. Orange grading is a crucial step in the fruit industry, as it helps to sort oranges according to different
criteria such as size, quality, ripeness, and health condition, ensuring safety for human consumption and better
price allocation and client satisfaction. Automated grading enables faster processing, precision, and reduced
human labor. In this paper, we implement a deep learning-based solution for orange grading via machine
vision. Unlike typical grading systems that analyze fruits from a single view, we capture multiview images of
each single orange in order to enable a richer representation. Afterwards, we compose the acquired images into
one collage. This enables the analysis of the whole orange skin. We train a convolutional neural network
(CNN) on the composed images to grade the oranges into three classes, namely ‘good’, ‘bad’, and ‘undefined’.
We also evaluate the performance with two different CNNs (ResNet-18 and SqueezeNet). We show
experimentally that multi-view grading is superior to single view grading.