Apple leaf diseases recognition based on ResNet-101 and CBAM

Abstract

Early identification and management of plant diseases is crucial to raising crop quality and yield. In order to create an effective deep learning model for autonomous apple leaf diseases identification, the issue of recognizing and classifying apple leaf infections is the main focus of this investigation. To improve the detection performance of the model, Convolutional Block Attention Module (CBAM) is added to the ResNet-101 deep convolutional neural network (CNN) model utilized in this work. According to the experimental findings, the ResNet-101 model with Convolutional Block Attention Module exhibits excellent performance when attempting to diagnose problems in apple leaves. Specifically, the ResNet-101+CBAM model outperforms the standard ResNet-101 model in the disease categories of 'Brown spot', 'Grey spot', 'Health' and 'Rust'. In some disease categories, such as 'Powdery mildew', the performance enhancement of the module (CBAM) is relatively insignificant due to the confusion caused by the similarity of features between the categories. Further analysis showed that the data enhancement technique had a significant positive impact on model performance.