A study on phenotypic micro-variation of stored melon based on weight loss rate

Abstract

This study proposes a novel deep learning algorithm that uses weight loss rate as a basis for classification and incorporates phenotypic micro-variation features for the dynamic prediction and analysis of melon storage processes. The Resnet50 backbone network was used as a basis to construct deformable residual blocks for nonrigid objects with high similarity over time, allowing the convolutional kernel shape to adapt to the melon surface shape. A hybrid spatial and channel attention mechanism was used to focus more attentional resources on the target regions requiring precise attention. The experimental results indicate that the proposed method outperforms traditional computer vision and machine learning methods in terms of effectiveness, performance, and generalization ability. We evaluate our method on a large dataset of melon fruit images and show that it outperforms existing methods in accurately predicting storage status and optimizing control strategies in time. The proposed method will serve as a powerful tool for other nondestructive detection and accurate prediction of the shelf life of large fruit.