Enhanced attention-driven hybrid deep learning with harris hawks optimizer for apple mechanical damage detection

Abstract

This study addresses the challenges of high costs and lengthy detection times associated with non-destructive testing of mechanical damage in apples. A novel approach combining deep learning and the Harris hawks optimizer (HHO) is proposed to tackle this. The study employs near-infrared relaxation spectroscopy to analyze apples’ spectral characteristics in different conditions. These spectral data are then processed by a residual network (ResNet) to extract relevant features. The extracted features are subsequently fed into a fusion model comprising long short-term memory (LSTM) and an Attention mechanism, with the model’s output determined by the Softmax function. The HHO is utilized to optimize parameter combinations for the search models, and its performance is compared against the gray wolf optimization algorithm whale optimization algorithm (WOA), and dwarf mongoose optimization algorithm. Moreover, the study introduces the Multiple Measurement Classification Recognition (MMCR) method to enhance accuracy. Comparative analyses demonstrate that the HHO-ResNet-LSTM (Attention)-MMCR model effectively captures intricate nonlinear relationships, resulting in an impressive accuracy increase to 98%. This innovative model offers a promising avenue for non-destructive fruit inspection, contributing to the advancement of inspection methodologies.