Intelligent Recognition of Goji Berry Pests Using CNN With Multi-Graphic-Occlusion Data Augmentation and Multiple Attention Fusion Mechanisms

Abstract

Goji berry is an important economic crop, yet pest infestations pose a significant threat to its yield and quality. Traditional pest identification mainly relies on manual inspection by experts with specialized knowledge, which is subjective, time-consuming, and labor-intensive. To address these issues, this experiment proposes an improved convolutional neural network (CNN) for accurate identification of 17 types of goji berry pests. Firstly, the original data set is augmented using a multi-graph-occlusion data augmentation method. Subsequently, the augmented data set is imported into the improved CNN for training. Based on the original ResNet18 model, a new CNN, named GojiNet, is constructed by embedding multi-attention fusion modules at appropriate locations. Experimental results demonstrate that GojiNet achieves an average recognition accuracy of 95.35%, representing a 2.60% improvement over the ResNet18 network. Notably, compared to the original network, the training time of this model increases only slightly, while its size is reduced, and the recognition accuracy is enhanced. The experiment verifies the performance of the GojiNet model through a series of evaluation indicators. This study confirms the tremendous potential and application prospects of deep learning in pest identification, providing a referential solution for intelligent and precise pest identification.