Deep Learning for Postharvest Decay Prediction in Apples

Artificial Intelligence (AI) is a widely used tool in precision agriculture for estimating the quality of food. It is especially relevant while assessing crops at various harvest and postharvest stages. Crop disease and damage detection is a task of top priority: some postharvest diseases or damages, e.g. decay, may destroy the crops and create toxins harmful to human beings. In this work, we apply U-Net, Deeplab, and Mask R-CNN models based on Convolutional Neural Networks (CNNs) for detecting and predicting the postharvest decay areas in stored apple fruits. Novelty of our approach is separate segmentation and prediction of postharvest decay and non-decay areas in apples. Images were acquired with a custom-made testbed consisting of a digital camera, stepper drivers pallet for apples, and the PC. The dataset of the acquired time-sequenced images of the postharvest apples includes 4440 images and is available online. Mask R-CNN demonstrated the best performance and achieved 98.81% of the mean Average Precision (mAP) for apples and 43.60% of the mAP for postharvest decay zones. The proposed approach is promising for improving the food storage process in precision agriculture.