ST-CFI: Swin Transformer with Convolutional Feature Interactions for Identifying Plant Diseases

Background: The increasing global population, coupled with the diminishing availability of arable land, has rendered the challenge of ensuring food security more pronounced. The prompt and precise identification of plant diseases is essential for reducing crop losses and improving agricultural yield. In this paper, we introduce the Swin Transformer with Convolutional Feature Interactions (STCFI) model, which represents a state-of-the-art deep learning methodology aimed at detecting plant diseases through the analysis of leaf images. The ST-CFI model effectively integrates the strengths of Convolutional Neural Networks (CNNs) and Swin Transformers, enabling the extraction of both local and global features from plant images. This is achieved through the implementation of an inception architecture and cross-channel feature learning, which collectively enhance the information necessary for detailed feature extraction. Results: We conducted a series of comprehensive experiments utilizing five distinct datasets: PlantVillage, the Plant Pathology 2021 competition, Plant- Doc, AI2018, and iBean. The ST-CFI model exhibited exceptional performance, achieving an accuracy of 99.94% on the PlantVillage dataset, 99.22% on iBean, 86.89% on AI2018, and 77.54% on PlantDoc. These results underscore the model’s robustness and its capacity to generalize across various datasets and real-world conditions. The high accuracy and F1 scores, in conjunction with low loss values, further validate the model’s efficacy in learning discriminative features. Conclusion: The ST-CFI model signifies a substantial advancement in the early and accurate detection of plant diseases, serving as a valuable instrument for precision agriculture. Its capacity to integrate CNNs and Transformers within a unified framework enhances the model’s feature extraction capabilities, resulting in improved accuracy in the identification of plant diseases. This study concludes that the ST-CFI model is an effective tool for addressing the challenges associated with plant disease detection, with significant implications for agricultural sustainability and productivity.