A Comprehensive Approach Toward Wheat Leaf Disease Identification Leveraging Transformer Models and Federated Learning

Abstract

Wheat is one of the most extensively cultivated crops worldwide that contributes significantly to global food caloric and protein production and is grown on millions of hectares yearly. However, diseases like brown rust, septoria, yellow rust, and other fungus diseases pose notable threats to wheat crops, impacting production and quality. Diagnosing these diseases is challenging, especially in areas with limited agricultural experts. Thus, creating computerized disease identification and decision-support technologies is crucial for safeguarding wheat leaf preservation and crop loss mitigation. The traditional approach to integrating data gathering and model training has substantial challenges in terms of data confidentiality, availability, and the costs related to data transmission. To address these challenges, federated learning (FL) is an appealing and effective option. Our study focuses on applying FL to classify agricultural diseases using image analysis. In our study, we conduct experiments on high-parameterized transfer learning (TL) models along with our proposed architecture based on the attention mechanism, introducing these models into a distributed learning strategy founded in FL. Our proposed architecture leverages the beneficial interactions of two cutting-edge vision transformer models including the advanced depthwise incorporating self-attention model referred to as CoAtNets, and the enhanced Swin Transformer V2, resulting in enhanced feature representation. Moreover, we introduce weight pruning into our model which is further classified by a reinforced linear attention mechanism (LA) to lower output dimensions. Our pruned lightweight (32M parameters) considerably decreases inference time with 624.249 ms and 644.899 on devices with low computational power, making it highly efficient in FL-based systems. The proposed model in our FL system significantly outperforms all other tested transfer learning models, including ConvNeXtBase, ConvNeXtLarge, EfficientNetV2L, InceptionResNetV2, ResNet152, and NASNetLarge, achieving accuracies up to 98% and 99%, precision up to 98%, recall up to 98%, and F-1 scores up to 95% across multiple input dimensions for wheat leaf disease classification.