ISNM-Net: A Deep Synergic CNN Architecture for Accurate ‎Grape Leaf Disease Classification Using Mobilenet and Res-‎Net

  • Authors

    • Alvin Ancy A. Electronics and Communications Engineering, Rajalakshmi Institute of Technology, Chennai
    • Vidhushavarshini Sureshkumar Computer and Communication Engineering, Rajalakshmi Institute of Technology, Chennai
    • M. Kiruthiga Devi Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani campus, Chennai
    • T. Rathi Devi Computer Science and Engineering, Christ University, Bangalore
    • A. Joshi Department of Artificial Intelligence and Data Science, Panimalar Engineering College, Chennai
    • Shobana S. Department of Information Technology, RMK College of Engineering, Chennai
    • C. Sharmila Department of Information Technology, Dr MGR Educational and Research Institute, Chennai
    https://doi.org/10.14419/gxnrnh89

    Received date: November 20, 2025

    Accepted date: January 2, 2026

    Published date: January 11, 2026

  • Deep Learning; Grape Leaf Disease; ISNM; Mobile-Net; Plant Village; Precision Viticulture; ResNet-50; Transfer Learning

  • Abstract

    Grape leaf diseases cause serious problems for viticulture around the world by having a substantial impact on the output and quality of ‎grapevine cultivation. Conventional manual diagnosis techniques are laborious, subjective, and frequently ineffectual in extensive field set-‎tings. In this study, we present the Inceptive Synergic Network Model (ISNM), a revolutionary deep learning framework for the precise, ‎effective, and scalable categorization of grape leaf diseases. With a Scale-Invariant Feature Learning (SIFL) module to improve spatial in-‎variance and reduce superfluous background noise, ISNM combines the advantages of Mobile-Net and ResNet-50 as dual backbones for ‎reliable multi-scale feature extraction. We also investigate how wavelet-based sub-band decomposition can enhance feature localization in a ‎variety of illumination and disease-spread scenarios. Lightweight convolutional layers optimize the fused deep features, allowing for de-‎ployment on edge devices for real-time monitoring. The suggested ISNM outperforms baseline CNNs and other conventional designs in ‎terms of precision, recall, and computational efficiency, achieving a state-of-the-art accuracy of 98.75% when tested on the Plant Village ‎grape leaf dataset. With potential uses in precision farming, self-sufficient vineyard monitoring, and sustainable crop management, this ‎work provides a scalable, interpretable, and useful approach to early grapevine disease diagnosis‎.

  • References

    1. Aher, P. G., Sabnis, V., & Jain, J. K. (2025). Deep learning for grape leaf disease detection: A review. Multidisciplinary Reviews, 8(11), 2025364-2025364. https://doi.org/10.31893/multirev.2025364.
    2. Ali, A., Ali, S., Husnain, M et al. (2022). Detection of deficiency of nutrients in grape leaves using deep network. Mathematical Problems in Engi-neering, 2022(1), 3114525. https://doi.org/10.1155/2022/3114525.
    3. Andrushia, A. D., & Patricia, A. T. (2020). Artificial bee colony optimization (ABC) for grape leaves disease detection. Evolving Systems, 11(1), 105-117. https://doi.org/10.1007/s12530-019-09289-2.
    4. Ashokkumar, K., Parthasarathy, S., Nandhini, S., & Ananthajothi, K. (2022). Prediction of grape leaf through digital image using FRCNN. Measurement: Sensors, 24, 100447.Billa, S. R., Malik, V., Bharath, E., & Sharma, S. Grapevine fruits disease detection using different deep learning models. Multimedia Tools and Applications, 84(9), 5523–5548 (2025). https://doi.org/10.1016/j.measen.2022.100447.
    5. Boulent, J., Foucher, S., Théau, J., & St-Charles, P. L. (2019). Convolutional neural networks for the automatic identification of plant diseas-es. Frontiers in plant science, 10, 941. https://doi.org/10.3389/fpls.2019.00941.
    6. Canghai, W., Xingxiang, G., Huanliang, X., & Huixin, H. (2025). Fine-grained recognition of grape leaf diseases based on transfer learning and convolutional block attention module. Applied Soft Computing, 172, 112896. https://doi.org/10.1016/j.asoc.2025.112896.
    7. Chen, Y., & Wu, Q. (2023). Grape leaf disease identification with sparse data via generative adversarial networks and convolutional neural net-works. Precision Agriculture, 24(1), 235-253. https://doi.org/10.1007/s11119-022-09941-z.
    8. Elfatimi, E., Eryigit, R., & Elfatimi, L. (2022). Beans leaf diseases classification using mobilenet models. Ieee Access, 10, 9471-9482. https://doi.org/10.1109/ACCESS.2022.3142817.
    9. 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.
    10. Guo, W., Feng, Q., Li, X., Yang, S., & Yang, J. (2022). Grape leaf disease detection based on attention mechanisms. International Journal of Agri-cultural and Biological Engineering, 15(5), 205-212. https://doi.org/10.25165/j.ijabe.20221505.7548.
    11. Javidan, S. M., Banakar, A., Vakilian, K. A., & Ampatzidis, Y. (2023). Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning. Smart Agricultural Technology, 3, 100081. https://doi.org/10.1016/j.atech.2022.100081.
    12. Karim, M. J., Goni, M. O. F., Nahiduzzaman, M., Ahsan, M., Haider, J., & Kowalski, M. Enhancing agriculture through real-time grape leaf disease classification via an edge device with a lightweight CNN architecture and Grad-CAM. Scientific Reports, 14(1), 16022 (2024). Kaur, N., & Devendran, V. (2024). A novel framework for semi-automated system for grape leaf disease detection. Multimedia Tools and Applications, 83(17), 50733-50755. https://doi.org/10.1038/s41598-024-66989-9.
    13. Khan, M. A., Rehman, A., Saba, T., & Mehmood, Z. Grape leaf disease classification using transfer learning with VGG-16. Computers and Elec-tronics in Agriculture, 198, 107099 (2022).
    14. Rabbi, M. F., Zohra, F. T., Hossain, F., Akhi, N. N., Khan, S., Mahbub, K., & Biswas, M. (2022, December). Autism spectrum disorder detection using transfer learning with VGG 19, inception V3 and DenseNet 201. In International Conference on Recent Trends in Image Processing and Pat-tern Recognition (pp. 190-204). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-23599-3_14
    15. Kunduracioglu, I., & Pacal, I. (2024). Advancements in deep learning for accurate classification of grape leaves and diagnosis of grape diseas-es. Journal of Plant Diseases and Protection, 131(3), 1061-1080. https://doi.org/10.1007/s41348-024-00896-z.
    16. Liu, B., Ding, Z., Tian, L., He, D., Li, S., & Wang, H. (2020). Grape leaf disease identification using improved deep convolutional neural net-works. Frontiers in plant science, 11, 1082. https://doi.org/10.3389/fpls.2020.01082.
    17. Mamun, S. B., Payel, I. J., Ahad, M. T., Atkins, A. S., Song, B., & Li, Y. (2025). Grape Guard: A YOLO-based mobile application for detecting grape leaf diseases. Journal of Electronic Science and Technology, 23(1), 100300. https://doi.org/10.1016/j.jnlest.2025.100300.
    18. Rahman, K. N., Banik, S. C., Islam, R., & Al Fahim, A. (2025). A real time monitoring system for accurate plant leaves disease detection using deep learning. Crop Design, 4(1), 100092. https://doi.org/10.1016/j.cropd.2024.100092.
    19. Sagar, N., Suresh, K. P., Sridhara, S., Patil, B., Archana, C. A., Sekar, Y. S., ... & Sushma, R. (2025). Precision detection of grapevine downy and powdery mildew diseased leaves and fruits using enhanced ResNet50 with batch normalization. Computers and Electronics in Agriculture, 232, 110144. https://doi.org/10.1016/j.compag.2025.110144.
    20. Sozzi, M., Cantalamessa, S., Cogato, A., Kayad, A., & Marinello, F. (2022). Automatic bunch detection in white grape varieties using YOLOv3, YOLOv4, and YOLOv5 deep learning algorithms. Agronomy, 12(2), 319. https://doi.org/10.3390/agronomy12020319.
    21. Talaat, F. M., Shams, M. Y., Gamel, S. A., & ZainEldin, H. (2025). DeepLeaf: an optimized deep learning approach for automated recognition of grapevine leaf diseases. Neural Computing and Applications, 1-25. https://doi.org/10.1007/s00521-025-11038-3.
    22. Thanh, Long Dang, et al. "Optimization of extraction of total flavonoid from leafs of Helicteres hirsuta Lour. based on bath ultrasound-assisted and testing some biological activities." Research Journal of Biotechnology,19(1) (2024). https://doi.org/10.25303/1901rjbt071078.
    23. Wang, C., Wang, L., Ma, G., & Zhu, L. (2025). CSF-YOLO: A Lightweight Model for Detecting Grape Leafhopper Damage Lev-els. Agronomy, 15(3), 741. https://doi.org/10.3390/agronomy15030741.
    24. Xie, X., Ma, Y., Liu, B., He, J., Li, S., & Wang, H. (2020). A deep-learning-based real-time detector for grape leaf diseases using improved convo-lutional neural networks. Frontiers in plant science, 11, 751. https://doi.org/10.3389/fpls.2020.00751.
    25. Zhang, Y., Wa, S., Zhang, L., & Lv, C. (2022). Automatic plant disease detection based on tranvolution detection network with GAN modules using leaf images. Frontiers in Plant Science, 13, 875693. https://doi.org/10.3389/fpls.2022.875693.
    26. Gong, J., Zhang, H., Zeng, Y., Cheng, Y., Sun, X., & Wang, P. (2022). Combining BN-PAGE and microscopy techniques to investigate pigment-protein complexes and plastid transitions in citrus fruit. Plant Methods, 18(1), 124. https://doi.org/10.1186/s13007-022-00956-1.
    27. Guo, Z., Wang, C., Yang, G., Huang, Z., & Li, G. (2022). Msft-yolo: Improved yolov5 based on transformer for detecting defects of steel sur-face. Sensors, 22(9), 3467. https://doi.org/10.3390/s22093467.

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  • How to Cite

    A. , A. A., Sureshkumar , V. ., Devi , M. K. ., Devi , T. R. ., Joshi , A. ., S., S. . ., & Sharmila, C. . (2026). ISNM-Net: A Deep Synergic CNN Architecture for Accurate ‎Grape Leaf Disease Classification Using Mobilenet and Res-‎Net. International Journal of Basic and Applied Sciences, 15(1), 49-58. https://doi.org/10.14419/gxnrnh89