Fine-grained apple leaf disease recognition method based on Mask Auto-Encoder and Weak Supervised approach

Apple, as one of the largest fruit varieties in terms of planting yield and planting area in China, plays an important role in agricultural production. However, the development of apple industry is limited by apple leaf diseases, which pose a serious threat to apple yield and quality. In recent years, deep learning technology has made significant progress in image recognition. Based on the Masked Auto-Encoder (MAE) self-supervised pre-training method and weak supervision method, this study proposes a fine-grained apple leaf disease recognition method. The model uses the Masked Auto-Encoder (MAE) self-supervised pre-training method to extract features from apple leaf images to obtain accurate high-dimensional feature representation. And using the Weak Supervised label propagation process based on Bayesian method, a large amount of unlabeled data is collated as a supplement, thus achieving accurate recognition of apple leaf diseases. The experimental results show that the proposed method exhibits high accuracy and robustness in apple leaf disease recognition task. Compared with general deep learning, this method can improve the recognition accuracy of difficult samples and has strong generalization ability, which can adapt to apple leaf image recognition tasks under different environments.