Apple Leaf Disease Recognition Based on Improved Convolutional Neural Network with an Attention Mechanism

Traditional plant disease recognition algorithms have a complicated approach, difficult feature extraction, and low recognition accuracy. Based on the improved EfficientNetV2 model, this research classifies images of apple leaf disease. This study collected images of seven common apple leaf disease categories and one healthy category to address the present needs of various complex disease recognition scenarios. The disease images not only contain the common laboratory background but also add the background of the field growth environment of apple trees. And different recognition scenarios are further enriched by image enhancement techniques. For the model part, the processing of spatial feature information was strengthened while focusing on the channel feature information to ensure that the model focuses more on the subtle disease spot information for different disease classifications. The experimental results show that the accuracy of the model training recognition is 97.49%. To better evaluate this study, comparison experiments were conducted with five other popular convolutional neural network classification models, such as ResNet-50, DenseNet-121, Xception, MobileNet, and EfficientNet-B3. The improved models enhance the recognition accuracy of complex scenes and improve the model parameters and training speed. It provides a reference for apple leaf disease recognition and the development needs of smart agriculture.