Grading Support System for Pear Fruit Using Edge Computing

Le Lectier pears (hereafter, Pears) are graded based on appearance, requiring farmers to inspect tens of thousands in a short time before shipment. To assist in this process, a grading support system was developed. The existing cloud-based system used mobile devices to capture images and analyzed them with Convolutional Neural Networks (CNNs) and texture-based algorithms. However, communication delays and algorithm inefficiencies resulted in a 30 s execution time, posing a problem. This paper proposes an edge computing-based system using Mask R-CNN for appearance deterioration detection. Processing on edge servers reduces execution time to 5–10 s, and 39 out of 51 Pears are accurately detected.