ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases
Abstract 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. T...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-08673-0 |
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| author | Sheng Yu Li Xie Liang Dai |
| author_facet | Sheng Yu Li Xie Liang Dai |
| author_sort | Sheng Yu |
| collection | DOAJ |
| description | Abstract 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. This paper introduces the Swin Transformer with Convolutional Feature Interactions (ST-CFI), a state-of-the-art deep learning framework designed for detecting plant diseases through the analysis of leaf images. The ST-CFI model effectively integrates the strengths of the 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. Comprehensive experiments were conducted using five distinct datasets: PlantVillage, Plant Pathology 2021 competition dataset, PlantDoc, AI2018, and iBean. The ST-CFI model exhibited exceptional performance, achieving an accuracy of 99.96% 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. 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 effectively addresses plant disease detection challenges, with significant implications for agricultural sustainability and productivity. |
| format | Article |
| id | doaj-art-12e3fde47cff49cabff6d62b5f118b3b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-12e3fde47cff49cabff6d62b5f118b3b2025-08-20T04:02:46ZengNature PortfolioScientific Reports2045-23222025-07-0115112010.1038/s41598-025-08673-0ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseasesSheng Yu0Li Xie1Liang Dai2School of Information Engineering, Shaoguan UniversitySchool of Information Engineering, Shaoguan UniversitySchool of Big Data and Computer Science, GuiZhou Normal UniversityAbstract 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. This paper introduces the Swin Transformer with Convolutional Feature Interactions (ST-CFI), a state-of-the-art deep learning framework designed for detecting plant diseases through the analysis of leaf images. The ST-CFI model effectively integrates the strengths of the 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. Comprehensive experiments were conducted using five distinct datasets: PlantVillage, Plant Pathology 2021 competition dataset, PlantDoc, AI2018, and iBean. The ST-CFI model exhibited exceptional performance, achieving an accuracy of 99.96% 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. 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 effectively addresses plant disease detection challenges, with significant implications for agricultural sustainability and productivity.https://doi.org/10.1038/s41598-025-08673-0Plant disease identificationDeep learningSwin TransformerConvolutional Feature InteractionsVision Transformers |
| spellingShingle | Sheng Yu Li Xie Liang Dai ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases Scientific Reports Plant disease identification Deep learning Swin Transformer Convolutional Feature Interactions Vision Transformers |
| title | ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases |
| title_full | ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases |
| title_fullStr | ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases |
| title_full_unstemmed | ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases |
| title_short | ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases |
| title_sort | st cfi swin transformer with convolutional feature interactions for identifying plant diseases |
| topic | Plant disease identification Deep learning Swin Transformer Convolutional Feature Interactions Vision Transformers |
| url | https://doi.org/10.1038/s41598-025-08673-0 |
| work_keys_str_mv | AT shengyu stcfiswintransformerwithconvolutionalfeatureinteractionsforidentifyingplantdiseases AT lixie stcfiswintransformerwithconvolutionalfeatureinteractionsforidentifyingplantdiseases AT liangdai stcfiswintransformerwithconvolutionalfeatureinteractionsforidentifyingplantdiseases |