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<p>Tea cultivation holds significant economic and agricultural value across many regions, particularly in developing countries, where it supports millions of livelihoods. However, the productivity and quality of tea leaves are severely threatened by a wide range of foliar diseases, which, if undetected or misdiagnosed, can lead to substantial crop losses. Existing manual and traditional image-based detection methods are time-consuming and often lack the precision required for early-stage identification across multiple disease types. To address these limitations, this study proposes TeaLeafNet-GWO, an intelligent deep learning framework that integrates a hybrid CNN–Transformer architecture with Gray Wolf Optimization (GWO) for automated tea leaf disease classification. The model combines the spatial feature extraction power of CNNs with the global contextual awareness of Transformer blocks (MobileViT), enhanced by channel-wise recalibration using a squeeze-and-excitation network (SENet). GWO is employed to fine-tune critical hyperparameters, ensuring optimal performance and generalization. The dataset comprises 999 high-resolution images representing eight distinct tea leaf conditions, including both healthy and diseased classes. Preprocessing techniques such as contrast-limited adaptive histogram equalization (CLAHE) and median filtering were applied to enhance image quality and suppress noise. The model was trained and evaluated using a stratified split (70% training, 30% testing). TeaLeafNet-GWO achieved an average accuracy of 97.72%, F1-score of 86.66%, and Matthews correlation coefficient (MCC) of 84.44% in the training phase, with similarly high performance in testing, outperforming several state-of-the-art hybrid models in comparative analysis. These results demonstrate TeaLeafNet-GWO’s effectiveness as a lightweight, scalable, and accurate solution for real-time disease monitoring, offering tangible benefits to precision agriculture and smart farming systems.</p>

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Tealeafnet-gwo: an intelligent CNN-Transformer hybrid framework for tea leaf disease detection using gray wolf optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月10日

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  • Md Firoz Kabir,
  • Irfan Sadiq Rahat,
  • Charles Beverley,
  • Roise Uddin,
  • Shashi Kant

摘要

Tea cultivation holds significant economic and agricultural value across many regions, particularly in developing countries, where it supports millions of livelihoods. However, the productivity and quality of tea leaves are severely threatened by a wide range of foliar diseases, which, if undetected or misdiagnosed, can lead to substantial crop losses. Existing manual and traditional image-based detection methods are time-consuming and often lack the precision required for early-stage identification across multiple disease types. To address these limitations, this study proposes TeaLeafNet-GWO, an intelligent deep learning framework that integrates a hybrid CNN–Transformer architecture with Gray Wolf Optimization (GWO) for automated tea leaf disease classification. The model combines the spatial feature extraction power of CNNs with the global contextual awareness of Transformer blocks (MobileViT), enhanced by channel-wise recalibration using a squeeze-and-excitation network (SENet). GWO is employed to fine-tune critical hyperparameters, ensuring optimal performance and generalization. The dataset comprises 999 high-resolution images representing eight distinct tea leaf conditions, including both healthy and diseased classes. Preprocessing techniques such as contrast-limited adaptive histogram equalization (CLAHE) and median filtering were applied to enhance image quality and suppress noise. The model was trained and evaluated using a stratified split (70% training, 30% testing). TeaLeafNet-GWO achieved an average accuracy of 97.72%, F1-score of 86.66%, and Matthews correlation coefficient (MCC) of 84.44% in the training phase, with similarly high performance in testing, outperforming several state-of-the-art hybrid models in comparative analysis. These results demonstrate TeaLeafNet-GWO’s effectiveness as a lightweight, scalable, and accurate solution for real-time disease monitoring, offering tangible benefits to precision agriculture and smart farming systems.