Advancing plant disease classification: A robust and generalized approach with transformer-fused convolution and Wasserstein domain adaptation

Plant diseases pose significant threats to agricultural productivity and food security. Owing to a scarcity of field environment datasets, the prevailing plant disease classification approaches, trained on laboratory-controlled datasets, often grapple with achieving optimal performance in real-world environments. We proposed a novel and robust framework for Unsupervised Domain Adaptation (UDA), employing an adversarial learning approach with a Wasserstein distance-informed algorithm to learn domain invariant feature representations capable of generalizing more diverse features. This approach incorporates insights from a labeled source domain and adopts an unlabeled target domain by minimizing the distribution discrepancies between domains. Recently, mobile vision transformer (MViT)-based methods have been applied to UDA due to their ability to capture long-distance feature dependencies. However, these methods overlook the fact that MViT lacks effectiveness in extracting local feature details. The proposed framework combines the advantages of convolutional neural networks (CNNs) and MViTs, integrating local features extracted by CNNs with global features captured by MViTs. This fusion of local and global representations enhances transferability and feature discriminability within the domains. Furthermore, we incorporate a feature-fusing method to align channel dimensions and enhance the local details of the global representation. Extensive experiments using three plant disease datasets demonstrate the effectiveness and efficiency of our approach, yielding significant improvements in classification performance with 13.67%, compared to state-of-the-art (SOTA) and baseline methods. Our framework offers a promising solution for robust and efficient plant disease classification, providing valuable insights for sustainable agriculture and crop management.