Implementation Of A Hybrid Cnn-Transformer Model For Tomato Leaf Disease Detection Under Background Noise
Keywords:
tomato leaf disease; Hybrid CNN-Transformer; MobileNetV3-Large; Vision Transformer; PlantDoc; background noise; Grad-CAM.
Abstract
Tomato leaf diseases can significantly reduce crop productivity, while automated disease detection remains challenging when images are captured under complex field conditions with background noise. This study implements a Hybrid CNN-Transformer model for tomato leaf disease detection under background noise using the tomato subset of the PlantDoc dataset. The proposed architecture integrates MobileNetV3-Large for local feature extraction, a Feature Fusion Bridge for feature transformation, and a Vision Transformer encoder for global contextual representation. The dataset consists of 685 original images representing eight tomato leaf conditions and is divided into training, validation, and testing sets using an 80:10:10 stratified split. The proposed model is compared with pure MobileNetV3, ViT-B/16, and an EfficientNet-B0-based hybrid model. Experimental results show that the proposed Hybrid CNN-Transformer achieves 64.86% accuracy, 67.29% precision, 64.86% recall, and 64.49% F1-score on the independent test set. The model outperforms both single-model baselines by 5.40 percentage points in accuracy and the alternative hybrid model by 9.45 percentage points. Grad-CAM visualization indicates that the model primarily focuses on pathological leaf regions rather than irrelevant background elements. These findings demonstrate the potential of hybrid CNN-Transformer architectures for tomato disease classification under realistic background conditions.
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Zhang, X., Wang, G., & colleagues. (2024). Hybrid convolutional and Transformer-based approaches for tomato leaf disease recognition. Agronomy, 14, 673. https://doi.org/10.3390/agronomy14040673
Brahimi, M., Boukhalfa, K., & Moussaoui, A. (2017). Deep learning for tomato diseases: Classification and symptoms visualization. Applied Artificial Intelligence, 31(4), 299–315. https://doi.org/10.1080/08839514.2017.1315516
Chen, Z., Wang, G., Lv, T., & Zhang, X. (2024). Using a hybrid convolutional neural network with a Transformer model for tomato leaf disease detection. Agronomy, 14(4), 673. https://doi.org/10.3390/agronomy14040673
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16×16 words: Transformers for image recognition at scale. International Conference on Learning Representations. https://arxiv.org/abs/2010.11929
Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318. https://doi.org/10.1016/j.compag.2018.01.009
Fuentes, A., Yoon, S., Kim, S. C., & Park, D. S. (2017). A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17(9), 2022. https://doi.org/10.3390/s17092022
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778. https://doi.org/10.1109/CVPR.2016.90
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv. https://arxiv.org/abs/1704.04861
Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., Le, Q. V., & Adam, H. (2019). Searching for MobileNetV3. Proceedings of the IEEE/CVF International Conference on Computer Vision, 1314–1324. https://doi.org/10.1109/ICCV.2019.00140
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708. https://doi.org/10.1109/CVPR.2017.243
Hughes, D. P., & Salathé, M. (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv. https://arxiv.org/abs/1511.08060
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016
Karthik, R., Hariharan, M., Anand, S., Mathikshara, P., Johnson, A., & Menaka, R. (2020). Attention embedded residual CNN for disease detection in tomato leaves. Applied Soft Computing, 86, 105933. https://doi.org/10.1016/j.asoc.2019.105933
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin Transformer: Hierarchical vision Transformer using shifted windows. Proceedings of the IEEE/CVF International Conference on Computer Vision, 10012–10022. https://doi.org/10.1109/ICCV48922.2021.00986
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419. https://doi.org/10.3389/fpls.2016.01419
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4510–4520. https://doi.org/10.1109/CVPR.2018.00474
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, 618–626. https://doi.org/10.1109/ICCV.2017.74
Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., & Batra, N. (2020). PlantDoc: A dataset for visual plant disease detection. Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, 249–253. https://doi.org/10.1145/3371158.3371196
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 2016, Article 3289801. https://doi.org/10.1155/2016/3289801
Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning, 6105–6114. https://proceedings.mlr.press/v97/tan19a.html
Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 161, 272–279. https://doi.org/10.1016/j.compag.2018.03.032
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., & Jégou, H. (2021). Training data-efficient image Transformers & distillation through attention. Proceedings of the 38th International Conference on Machine Learning, 10347–10357. https://proceedings.mlr.press/v139/touvron21a.html
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1706.03762
Zhang, X., Wang, G., & colleagues. (2024). Hybrid convolutional and Transformer-based approaches for tomato leaf disease recognition. Agronomy, 14, 673. https://doi.org/10.3390/agronomy14040673
Published
2026-11-05
How to Cite
Mukti Adi Azhari, & I Made Artha Agastya. (2026). Implementation Of A Hybrid Cnn-Transformer Model For Tomato Leaf Disease Detection Under Background Noise. International Journal of Economics, Business and Innovation Research, 5(06), 3027-3037. https://doi.org/10.63922/ijebir.v5i06.7209
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