Dynamic Mode Decomposition and Deep Learning for Postharvest Decay Prediction in Apples

Abstract

The damages and diseases that may occur in apples during the storage and cannot be seen visually at early stages is a significant problem in precision agriculture leading to the loss of the lion share of crop. The difficulty of predicting postharvest degradation of apples while in storage is addressed in this article. For the prediction of postharvest decay zones, we used the Dynamic Mode Decomposition (DMD) method in conjunction with the Mask R-CNN model based on Convolutional Neural Networks (CNNs). For validating this idea we have designed a small greenhouse equipped with necessary sensors and actuators for simulating storage conditions for apples. We collected a dataset which includes 552 images of apples stored under the extreme temperatures and humidity. For DMD reconstructed images demonstrating the growth dynamics of decay in apples, the proposed approach achieves 0.999 Structural Similarity Index Measure (SSIM) and 66.813 Peak Signal to Noise Ratio (PSNR). Mask R-CNN predicted postharvest decay zones in apples with an Average Precision (AP) of 83.809% and F1-score with 83.307%. The proposed method is promising for enhancing food storage and postharvest control in precision agriculture since it is based on contactless sensing with video cameras.