MAFDE-DN4: Improved Few-shot plant disease classification method based on Deep Nearest Neighbor Neural Network

Abstract

Deep learning-based methods for accurately identifying plant diseases can be effective in improving crop yields. However, the effectiveness of these methods heavily relies on the availability of large-scale manually labeled datasets, which present technical and economic challenges. Few-shot learning methods can be generalized to new categories with a small number of samples, which is very promising in the field of plant disease recognition despite the limited sample size. However, due to the complexity of real-life scenarios, distribution of disease leaves, and significant intra-class variability and inter-class similarity resulting from crop species and disease species, existing methods perform poorly in the field of plant disease recognition. To address the above problems, this paper proposes an improved multi-scale attention fusion with discriminative enhancement deep nearest neighbor neural network MAFDE-DN4 based on DN4. Our approach makes three main contributions. First, we have designed a bidirectional weighted feature fusion module (BWFM) to enhance the aggregation of fine-grained features and enhance the network’s representation of complex disease images. Second, to tackle the issue of sparse feature descriptors being vulnerable to irrelevant noise in small sample conditions, a episodic attention module (EA) has been developed to produce scene category-relevant attention maps. This effectively mitigates the influence of irrelevant background information. Finally, we introduce additional spacing between category margins to enhance the original softmax loss function, amplify the inter-class differences to reduce the intra-class distances, and add L2 regularization constraint terms to stabilize the training process. To simulate different real-world scenarios, we set up different dataset settings. Under the 1-shot task and the 5-shot task, our method achieves 57.5% and 81.41% accuracy under the within-domain strategy and 36.54% and 51.23% accuracy under the cross-domain strategy. The experimental results show that our method outperforms existing related work in the field of plant disease recognition, whether it is a dataset with a single background or a field dataset with a complex background in a real scenario. MAFDE-DN4 based on Few-shot learning requires substantially less data on new categories of plant diseases.