关键词: 病害识别, 残差网络, 注意力机制, 余弦退火学习率, 迁移学习, 卷积块注意力模块, 多层感知机

Abstract:

To deal with the issues of low accuracy of apple leaf disease images recognition under weak supervision with only image category labeling, an improved CBAM-ResNet-based algorithm was proposed in this research. Using ResNet18 as the base model, the multilayer perceptron (MLP) in the lightweight convolutional block attention module (CBAM) attention mechanism channel was improved by up-dimensioning to amplify the details of apple leaf disease features. The improved CBAM attention module was incorporated into the residual module to enhance the key details of AlphaDropout with SeLU (Scaled Exponential Linearunits) to prevent overfitting of its network and accelerate the convergence effect of the model. Finally, the learning rate was adjusted using a single-cycle cosine annealing algorithm to obtain the disease recognition model. The training test was performed under weak supervision with only image-level annotation of all sample images, which greatly reduced the annotation cost. Through ablation experiments, the best dimensional improvement of MLP in CBAM was explored as 2. Compared with the original CBAM, the accuracy rate was increased by 0.32%, and the training time of each round was reduced by 8 s when the number of parameters increased by 17.59%. Tests were conducted on a dataset of 6185 images containing five diseases, including apple spotted leaf drop, brown spot, mosaic, gray spot, and rust, and the results showed that the model achieved an average recognition accuracy of 98.44% for the five apple diseases under weakly supervised learning. The improved CBAM-ResNet18 had increased by 1.47% compared with the pre-improved ResNet18, and was higher than VGG16, DesNet121, ResNet50, ResNeXt50, EfficientNet-B0 and Xception control model. In terms of learning efficiency, the improved CBAM-ResNet18 compared to ResNet18 reduced the training time of each round by 6 s under the condition that the number of parameters increased by 24.9%, and completed model training at the fastest speed of 137 s per round in VGG16, DesNet121, ResNet50, ResNeXt50, Efficient Net-B0 and Xception control models. Through the results of the confusion matrix, the average precision, average recall rate, and average F₁ score of the model were calculated to reach 98.43%, 98.46%, and 0.9845, respectively. The results showed that the proposed improved CBAM-ResNet18 model could perform apple leaf disease identification and had good identification results, and could provide technical support for intelligent apple leaf disease identification providing.

Key words: disease identification, residual network, attentional mechanisms, cosine annealing learning rate, transfer learning, convolutional block attention module (CBAM), multilayer perceptron (MLP)