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Full Research Article

ResNet-20: A Deep Learning Approach for Accurate Classification and Identification of Legume Leaf Diseases

B
Babasaheb Dnyandeo Patil1
0000-0002-6339-9595
G
Geetika Parmar2
0000-0002-4137-7811
M
Manisha Shinde-Pawar3
0000-0002-8276-9782
D
Deepali Shahane4
0000-0002-3099-9009
A
Abhijit Ashok Patil5
0000-0003-0767-6436
V
Vaibhav Dhotare6,*
0009-0001-7270-060X
1Department of Computer Applications, Bharati Vidyapeeth (Deemed to be University) Pune, Institute of Management and Rural Development Administration, Sangli-416 416, Maharashtra, India.
2Department of Computer Science and Application, Dr. Vishwanath Karad MIT World Peace University, Pune-411 045, Maharashtra, India.
3Department of Computer Applications, Kasegaon Education Society’s Rajarambapu Institute of Technology, affiliated to Shivaji University, Sakharale-415 414, Maharashtra, India.
4School of Computer Science and Applications, Dr. Vishwanath Karad MIT World Peace University, Pune-411 038, Maharashtra, India.
5Department of Computer Applications, Bharati Vidyapeeth (Deemed to be University), Pune, Yashwantrao Mohite Institute of Management, Karad-416 416, Maharashtra, India.
6Department of Computer Science and Engineering, Kasegaon Education Society’s Rajarambapu Institute of Technology, affiliated to Shivaji University, Sakharale-415 414, Maharashtra, India.

ABSTRACT

Background: The necessity for effective and precise disease detection techniques is highlighted by the rising demand for legumes. Convolutional Neural Networks (CNNs), a type of deep learning, provide a potent way to diagnose plant diseases. CNNs make it possible to accurately identify illnesses in real time by quickly evaluating enormous amounts of plant pictures. By giving farmers proactive tools for monitoring crop health, cutting losses and enhancing food quality, automated detection systems can improve agricultural practices.
Methods: To categorize bean leaves, this study suggests a deep learning-based method utilizing the ResNet-20 model. To increase model generalization and lessen overfitting, data augmentation techniques such as scaling, rotation and flipping were employed. The model was trained on a dataset of labelled images and its performance was assessed using categorization metrics, confusion matrix, ROC curve and Matthews Correlation Coefficient.
Result: The ResNet-20 model’s test accuracy was 76.15%. Additional performance indicators such as metrices demonstrated the model’s reliable classification abilities. The ROC curve further illustrated the model’s exceptional ability to differentiate between healthy and unhealthy leaves.

REFERENCES


  1. Buja, I., Sabella, E., Monteduro, A.G., Chiriacò, M.S., De Bellis, L., Luvisi, A. and Maruccio, G. (2021). Advances in plant disease detection and monitoring: From traditional assays to in-field diagnostics. Sensors. 21(6): 2129. https:// doi.org/10.3390/s21062129

  2. Dong, R., Shiraiwa, A., Pawasut, A., Sreechun, K. and Hayashi, T. (2024). Diagnosis of citrus greening using artificial intelligence: A faster region-based convolutional neural network approach with convolution block attention module- integrated VGGNet and ResNet models. Plants. 13(12): 1631. https://doi.org/10.3390/plants13121631.

  3. FAO. (2024). The State of Plant Health in the World: Global Challenges and Opportunities. Food and Agriculture Organization of the United Nations. https://www.fao.org/ state-of-plant-health/en/

  4. Kalaivani, S., Tharini, C., Viswa, T.M.S., Sara, K.Z.F. and Abinaya, S.T. (2024). RESNET-based classification for leaf disease detection. Journal of the Institution of Engineers (India) Series B. https://doi.org/10.1007/s40031-024-01062-7.

  5. Khan, S.D., Basalamah, S. and Naseer, A. (2024). Classification of plant diseases in images using dense-inception architecture with attention modules. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-19860-y

  6. Kim, S.Y. and AlZubi, A.A. (2024). Blockchain and artificial intelligence for ensuring the authenticity of organic legume products in supply chains. Legume Research – An International Journal. 47(7): 1144-1150. doi: 10.18805/LRF-786.

  7. Kumar, V., Arora, H. and Sisodia, J. (2020). ResNet-based Approach for Detection and Classification of Plant Leaf Diseases. In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE. (pp. 495-502). https://doi.org/10.1109/ICESC48915.2020.9155585.

  8. Min, P.K., Mito, K. and Kim, T.H. (2024). The evolving landscape of artificial intelligence applications in animal health. Indian Journal of Animal Research. 58(10): 1793-1798. doi: 10.18805/IJAR.BF-1742.

  9. Mostafa, A., Alnuaim, A. and AlZubi, A.A. (2025). Utilizing convolutional neural networks for accurate detection of leaf diseases in fava beans. Legume Research- An International Journal. 48(3): 494–502. doi: 10.18805/LRF-823.

  10. Negi, P. and Anand, S. (2024). Plant disease detection, diagnosis and management: Recent advances and future perspectives. In: Artificial Intelligence and Smart Agriculture. [K. Pandey, N.L. Kushwaha, C.B. Pande and K.G. Singh (Eds.)], Advances in Geographical and Environmental Sciences. Springer. (pp. 359-374). https://doi.org/10.1007/978-981-97-0341-8_20.

  11. Peng, Y. and Wang, Y. (2022). Leaf disease image retrieval with object detection and deep metric learning. Frontiers in Plant Science. 13: 963302. https://doi.org/10.3389/ fpls.2022.963302.

  12. Picon, A., Alvarez-Gila, A., Seitz, M., Ortiz-Barredo, A., Echazarra, J. and Johannes, A. (2019). Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Computers and Electronics in Agriculture. 161: 280-290.

  13. Ramesh Babu, P., Srikrishna, A. and Gera, V. R. (2024). Diagnosis of tomato leaf disease using OTSU multi-threshold image segmentation-based chimp optimization algorithm and LeNet-5 classifier. Journal of Plant Diseases and Protection. https://doi.org/10.1007/s41348-024-00953-7.

  14. Richey, B., Majumder, S., Shirvaikar, M. and Kehtarnavaz, N. (2020). Real-time Detection of Maize Crop Disease via a Deep Learning-Based Smartphone App. In: Real-time Image Processing and Deep Learning. International Society for Optics and Photonics. (p. 114010A).

  15. Roshini, P., Khajavali, S., Snigdha, M.L.S., Harsha, B.D., Srilakshmi, B. and Gopi, A. (2024). CNN Design with AlexNet Algorithm for Diagnosis of Diseases in Cassava Leaves. 2024 International Conference on Expert Clouds and Applications (ICOECA), 1-8. https://doi.org/10.1109/ICOECA62351. 2024.00129.

  16. Shahoveisi, F., Gorji, H.T., Shahabi, S., Hosseinirad, S., Markell, S. and Vasefi, F. (2023). Application of image processing and transfer learning for the detection of rust disease. Scientific Reports. 13(1). https://doi.org/10.1038/s41598- 023-31942-9.

  17. Serttaº, S. and Deniz, E. (2023). Disease detection in bean leaves using deep learning. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering. 65(2): 115-129. https://doi.org/10.33769/ aupse.1247233

  18. Singh, V., Chug, A. and Singh, A.P. (2023). Classification of beans leaf diseases using fine-tuned CNN model. Procedia Computer Science. 218: 348-356. https://doi.org/10.101 6/j.procs.2023.01.017.

  19. Suma, S.A., Haque, A., Vasker, N., Hasan, M., Ovi, J.A. and Islam, M. (2023). Beans Disease Detection using Convolutional Neural Network. In 2023 4th International Conference on Big Data Analytics and Practices (IBDAP), Bangkok, Thailand (pp. 1-5). https://doi.org/10.1109/IBDAP5858 1. 2023.10271983

  20. Tavakoli, H., Alirezazadeh, P., Hedayatipour, A., Nasib, A.B. and Landwehr, N. (2021). Leaf image-based classification of some common bean cultivars using discriminative convolutional neural networks. Computers and Electronics in Agriculture. 181: 105935. https://doi.org/10.1016/j.compag.2020. 105935

  21. Yao, J., Tran, S. N., Sawyer, S. and Garg, S. (2023). Machine learning for leaf disease classification: Data, techniques and applications. Artificial Intelligence Review. 56(S3): 3571- 3616. https://doi.org/10.1007/s10462-023-10610-4.

  22. Zhang, M., Ma, W., Tao, R., Fan, Q., Zhang, M., Qin, D., Cao, X., Li, J., Xiong, R. and Huang, C. (2024). Nanomaterials: Recent advances in plant disease diagnosis and treatment. Nano Today. 57: 102326. https://doi.org/10.1016/j.nantod. 2024.102326

  23. Yao, J., Tran, S.N., Sawyer, S. and Garg, S. (2023). Machine learning for leaf disease classification: Data, techniques and applications. Artificial Intelligence Review. 56(S3): 3571- 3616. https://doi.org/10.1007/s10462-023-10610-4.
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Indian Journal of Agricultural Research
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    Full Research Article

    ResNet-20: A Deep Learning Approach for Accurate Classification and Identification of Legume Leaf Diseases

    B
    Babasaheb Dnyandeo Patil1
    0000-0002-6339-9595
    G
    Geetika Parmar2
    0000-0002-4137-7811
    M
    Manisha Shinde-Pawar3
    0000-0002-8276-9782
    D
    Deepali Shahane4
    0000-0002-3099-9009
    A
    Abhijit Ashok Patil5
    0000-0003-0767-6436
    V
    Vaibhav Dhotare6,*
    0009-0001-7270-060X
    1Department of Computer Applications, Bharati Vidyapeeth (Deemed to be University) Pune, Institute of Management and Rural Development Administration, Sangli-416 416, Maharashtra, India.
    2Department of Computer Science and Application, Dr. Vishwanath Karad MIT World Peace University, Pune-411 045, Maharashtra, India.
    3Department of Computer Applications, Kasegaon Education Society’s Rajarambapu Institute of Technology, affiliated to Shivaji University, Sakharale-415 414, Maharashtra, India.
    4School of Computer Science and Applications, Dr. Vishwanath Karad MIT World Peace University, Pune-411 038, Maharashtra, India.
    5Department of Computer Applications, Bharati Vidyapeeth (Deemed to be University), Pune, Yashwantrao Mohite Institute of Management, Karad-416 416, Maharashtra, India.
    6Department of Computer Science and Engineering, Kasegaon Education Society’s Rajarambapu Institute of Technology, affiliated to Shivaji University, Sakharale-415 414, Maharashtra, India.

    ABSTRACT

    Background: The necessity for effective and precise disease detection techniques is highlighted by the rising demand for legumes. Convolutional Neural Networks (CNNs), a type of deep learning, provide a potent way to diagnose plant diseases. CNNs make it possible to accurately identify illnesses in real time by quickly evaluating enormous amounts of plant pictures. By giving farmers proactive tools for monitoring crop health, cutting losses and enhancing food quality, automated detection systems can improve agricultural practices.
    Methods: To categorize bean leaves, this study suggests a deep learning-based method utilizing the ResNet-20 model. To increase model generalization and lessen overfitting, data augmentation techniques such as scaling, rotation and flipping were employed. The model was trained on a dataset of labelled images and its performance was assessed using categorization metrics, confusion matrix, ROC curve and Matthews Correlation Coefficient.
    Result: The ResNet-20 model’s test accuracy was 76.15%. Additional performance indicators such as metrices demonstrated the model’s reliable classification abilities. The ROC curve further illustrated the model’s exceptional ability to differentiate between healthy and unhealthy leaves.

    REFERENCES


    1. Buja, I., Sabella, E., Monteduro, A.G., Chiriacò, M.S., De Bellis, L., Luvisi, A. and Maruccio, G. (2021). Advances in plant disease detection and monitoring: From traditional assays to in-field diagnostics. Sensors. 21(6): 2129. https:// doi.org/10.3390/s21062129

    2. Dong, R., Shiraiwa, A., Pawasut, A., Sreechun, K. and Hayashi, T. (2024). Diagnosis of citrus greening using artificial intelligence: A faster region-based convolutional neural network approach with convolution block attention module- integrated VGGNet and ResNet models. Plants. 13(12): 1631. https://doi.org/10.3390/plants13121631.

    3. FAO. (2024). The State of Plant Health in the World: Global Challenges and Opportunities. Food and Agriculture Organization of the United Nations. https://www.fao.org/ state-of-plant-health/en/

    4. Kalaivani, S., Tharini, C., Viswa, T.M.S., Sara, K.Z.F. and Abinaya, S.T. (2024). RESNET-based classification for leaf disease detection. Journal of the Institution of Engineers (India) Series B. https://doi.org/10.1007/s40031-024-01062-7.

    5. Khan, S.D., Basalamah, S. and Naseer, A. (2024). Classification of plant diseases in images using dense-inception architecture with attention modules. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-19860-y

    6. Kim, S.Y. and AlZubi, A.A. (2024). Blockchain and artificial intelligence for ensuring the authenticity of organic legume products in supply chains. Legume Research – An International Journal. 47(7): 1144-1150. doi: 10.18805/LRF-786.

    7. Kumar, V., Arora, H. and Sisodia, J. (2020). ResNet-based Approach for Detection and Classification of Plant Leaf Diseases. In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE. (pp. 495-502). https://doi.org/10.1109/ICESC48915.2020.9155585.

    8. Min, P.K., Mito, K. and Kim, T.H. (2024). The evolving landscape of artificial intelligence applications in animal health. Indian Journal of Animal Research. 58(10): 1793-1798. doi: 10.18805/IJAR.BF-1742.

    9. Mostafa, A., Alnuaim, A. and AlZubi, A.A. (2025). Utilizing convolutional neural networks for accurate detection of leaf diseases in fava beans. Legume Research- An International Journal. 48(3): 494–502. doi: 10.18805/LRF-823.

    10. Negi, P. and Anand, S. (2024). Plant disease detection, diagnosis and management: Recent advances and future perspectives. In: Artificial Intelligence and Smart Agriculture. [K. Pandey, N.L. Kushwaha, C.B. Pande and K.G. Singh (Eds.)], Advances in Geographical and Environmental Sciences. Springer. (pp. 359-374). https://doi.org/10.1007/978-981-97-0341-8_20.

    11. Peng, Y. and Wang, Y. (2022). Leaf disease image retrieval with object detection and deep metric learning. Frontiers in Plant Science. 13: 963302. https://doi.org/10.3389/ fpls.2022.963302.

    12. Picon, A., Alvarez-Gila, A., Seitz, M., Ortiz-Barredo, A., Echazarra, J. and Johannes, A. (2019). Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Computers and Electronics in Agriculture. 161: 280-290.

    13. Ramesh Babu, P., Srikrishna, A. and Gera, V. R. (2024). Diagnosis of tomato leaf disease using OTSU multi-threshold image segmentation-based chimp optimization algorithm and LeNet-5 classifier. Journal of Plant Diseases and Protection. https://doi.org/10.1007/s41348-024-00953-7.

    14. Richey, B., Majumder, S., Shirvaikar, M. and Kehtarnavaz, N. (2020). Real-time Detection of Maize Crop Disease via a Deep Learning-Based Smartphone App. In: Real-time Image Processing and Deep Learning. International Society for Optics and Photonics. (p. 114010A).

    15. Roshini, P., Khajavali, S., Snigdha, M.L.S., Harsha, B.D., Srilakshmi, B. and Gopi, A. (2024). CNN Design with AlexNet Algorithm for Diagnosis of Diseases in Cassava Leaves. 2024 International Conference on Expert Clouds and Applications (ICOECA), 1-8. https://doi.org/10.1109/ICOECA62351. 2024.00129.

    16. Shahoveisi, F., Gorji, H.T., Shahabi, S., Hosseinirad, S., Markell, S. and Vasefi, F. (2023). Application of image processing and transfer learning for the detection of rust disease. Scientific Reports. 13(1). https://doi.org/10.1038/s41598- 023-31942-9.

    17. Serttaº, S. and Deniz, E. (2023). Disease detection in bean leaves using deep learning. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering. 65(2): 115-129. https://doi.org/10.33769/ aupse.1247233

    18. Singh, V., Chug, A. and Singh, A.P. (2023). Classification of beans leaf diseases using fine-tuned CNN model. Procedia Computer Science. 218: 348-356. https://doi.org/10.101 6/j.procs.2023.01.017.

    19. Suma, S.A., Haque, A., Vasker, N., Hasan, M., Ovi, J.A. and Islam, M. (2023). Beans Disease Detection using Convolutional Neural Network. In 2023 4th International Conference on Big Data Analytics and Practices (IBDAP), Bangkok, Thailand (pp. 1-5). https://doi.org/10.1109/IBDAP5858 1. 2023.10271983

    20. Tavakoli, H., Alirezazadeh, P., Hedayatipour, A., Nasib, A.B. and Landwehr, N. (2021). Leaf image-based classification of some common bean cultivars using discriminative convolutional neural networks. Computers and Electronics in Agriculture. 181: 105935. https://doi.org/10.1016/j.compag.2020. 105935

    21. Yao, J., Tran, S. N., Sawyer, S. and Garg, S. (2023). Machine learning for leaf disease classification: Data, techniques and applications. Artificial Intelligence Review. 56(S3): 3571- 3616. https://doi.org/10.1007/s10462-023-10610-4.

    22. Zhang, M., Ma, W., Tao, R., Fan, Q., Zhang, M., Qin, D., Cao, X., Li, J., Xiong, R. and Huang, C. (2024). Nanomaterials: Recent advances in plant disease diagnosis and treatment. Nano Today. 57: 102326. https://doi.org/10.1016/j.nantod. 2024.102326

    23. Yao, J., Tran, S.N., Sawyer, S. and Garg, S. (2023). Machine learning for leaf disease classification: Data, techniques and applications. Artificial Intelligence Review. 56(S3): 3571- 3616. https://doi.org/10.1007/s10462-023-10610-4.
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