Plant Disease Detection Algorithm Based on Efficient Swin Transformer

Plant diseases present a significant threat to global agricultural productivity, endangering both crop yields and quality. Traditional detection methods largely rely on manual inspection, a process that is not only labor-intensive and time-consuming but also subject to subjective biases and dependent on operators’ expertise. Recent advancements in Transformer-based architectures have shown substantial progress in image classification tasks, particularly excelling in global feature extraction. However, despite their strong performance, the high computational complexity and large parameter requirements of Transformer models limit their practical application in plant disease detection. To address these constraints, this study proposes an optimized Efficient Swin Transformer specifically engineered to reduce computational complexity while enhancing classification accuracy. This model is an improvement over the Swin-T architecture, incorporating two pivotal modules: the Selective Token Generator and the Feature Fusion Aggregator. The Selective Token Generator minimizes the number of tokens processed, significantly increasing computational efficiency and facilitating multi-scale feature extraction. Concurrently, the Feature Fusion Aggregator adaptively integrates static and dynamic features, thereby enhancing the model’s ability to capture complex details within intricate environmental contexts.Empirical evaluations conducted on the PlantDoc dataset demonstrate the model’s superior classification performance, achieving a precision of 80.14% and a recall of 76.27%. Compared to the standard Swin-T model, the Efficient Swin Transformer achieves approximately 20.89% reduction in parameter size while improving precision by 4.29%. This study substantiates the potential of efficient token conversion techniques within Transformer architectures, presenting an effective and accurate solution for plant disease detection in the agricultural sector.