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Full Research Article

Detailed Analysis and Advanced Classification of Bean Leaf Diseases using MobileNetV3

M
Minakshi C. Satape1
https://orcid.org/0009-0001-2130-8739
N
Namrata A. Jadhav1,*
https://orcid.org/0009-0003-9378-3103
D
Deelip A. Patil1
https://orcid.org/0000-0002-8302-0888
V
Venkata Naga Siva Kumar Challa2
https://orcid.org/0000-0002-2746-7685
S
Shalini Sharma3
https://orcid.org/0000-0002-9117-6954
S
Saba Tahseen4
https://orcid.org/0000-0001-7185-9312
1Department of Computer Applications, Kasegaon Education Society’s, Rajarambapu Institute of Technology, affiliated to Shivaji University, Sakharale-415 414, Maharashtra, India.
2KL Business School, Koneru Lakshmaiah Education Foundation, Guntur-521 180, Andhra Pradesh, India.
3Department of Language, Culture and Society, SRM Institute of Science and Technology, Delhi NCR Campus, Modinagar, Ghaziabad-201 204, Uttar Pradesh, India.
4Department of Computer Science and Business Systems, Dayananda Sagar College of Engineering, Bangalore-560 111, Karnataka, India.

ABSTRACT

Background: The timely and precise detection of foliage diseases is essential for the efficient management of bean crops. Conventional techniques for detecting leaf diseases need professionals to physically examine the leaves, which is laborious and subject to human mistakes. Due to advanced machine learning (ML) computations, convolutional neural networks (CNNs) are capable of examining leaf diseases. MobileNetV3 is particularly effective for edge device applications because of its lightweight and efficient design.

Methods: In this study, the effectiveness of MobileNetV3 in distinguishing three disease categories: Angular Leaf Spot, Bacterial Blight and Healthy, from images of bean leaves is evaluated. The dataset utilized in this study is sourced from Kaggle and comprises a total of 1295 images categorized into three classes. Each image was resized to 224´224 pixels and normalized to a range of [0, 1] to prepare it for input into the MobileNetV3 model. Data augmentation techniques, including rotation, flipping and zooming, were applied to enhance the model’s generalization capability. The MobileNetV3 architecture was implemented with a batch size of 32 and trained for 25 epochs using the Adam optimizer with a learning rate of 1e-4. Performance was evaluated based on accuracy, precision, recall and F1-score, using a split dataset consisting of training (80%), validation (10%) and test (10%) subsets.

Result: The MobileNetV3 model demonstrated a high level of performance in classifying bean leaf diseases. The model achieved an overall accuracy of 92.19% with a macro average F1-score of 92.27%. These results indicate that MobileNetV3 is effective for automated classification of bean leaf diseases, with high precision and recall, particularly for Angular Leaf Spot.

REFERENCES


  1. Abed, S.H., Al-Waisy, A.S., Mohammed, H.J. and Al-Fahdawi, S. (2021). A modern deep learning framework in robot vision for automated bean leaves diseases detection. International Journal of Intelligent Robotics and Applications. 5(2): 235-51. https://doi.org/10.1007/s41315-021-00174-3.

  2. Abed, S. and Esmaeel, A.A. (2018). A novel approach to classify and detect bean diseases based on image processing. In Proceedings of the 2018 IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE). pp.297-302. IEEE. https://doi.org/10.1109/ISCAIE. 2018. 8405488.

  3. CGIAR Initiative on Foresight. (2023). What do we know about the future of pulses in global and regional agri-food systems? https://www.cgiar.org/news-events/news/what-do- we-know-about-the-future-of-pulses-in-global-and- regional-agri-food-systems/.

  4. Abode, G. (2022). Bean disease dataset [Data set]. Kaggle. https:/ /www.kaggle.com/datasets/therealoise/bean-disease- dataset (Accessed July 25, 2024).

  5. Elfatimi, E., Eryigit, R. and Elfatimi, L. (2022). Beans leaf diseases classification using MobileNet models. IEEE Access. 10: 9471-9482. https://doi.org/10.1109/ACCESS.2022. 3142817.

  6. Food and Agriculture Organization (FAO). (2023). FAOSTAT Database. https://openknowledge.fao.org/server/api/ core/bitstreams/28cfd24e-81a9-4ebc-b2b5-4095fe 5b1dab/content/cc8166en.html [Accessed ON 25/07/ 2024].

  7. Gomez, D., Selvaraj, M.G., Casas, J., Mathiyazhagan, K., Rodriguez, M., Assefa, T., Mlaki, A., Nyakunga, G., Kato, F., Mukankusi, C., Girma, E., Mosquera, G., Arredondo, V. and Espitia, E. (2024). Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI. Scientific Reports. 14(1). https://doi.org/10.1038/s41598-024-66281-w.

  8. Gill, K.S., Anand, V., Gupta, R. and Pahwa, V. (2023). Bean leaf disease classification and visualization using deep learning techniques on sequential model. In Proceedings of the 2023 International Conference in Advances in Power, Signal and Information Technology (APSIT). pp. 14-18. IEEE. https://doi.org/10.1109/APSIT58554.2023. 10201712.

  9. Hazra, K.K., Nath, C.P., Ghosh, P.K. and Swain, D.K. (2020). Inclusion of legumes in rice-wheat cropping system for enhancing carbon sequestration. Carbon management in tropical and sub-tropical terrestrial systems. 23-36.

  10. Heuzé, V., Tran, G., Delagarde, R., Lessire, M. and Lebas, F. (2021). Faba bean (Vicia faba). Feedipedia, a programme by INRAE, CIRAD, AFZ and FAO. Retrieved from https:// www.feedipedia.org/node/4926.

  11. International Food Policy Research Institute (IFPRI). (2022). Global Food Policy Report: Climate Change and Food Systems. https://www.ifpri.org/?s=bean

  12. Jena, J., Maitra, S., Hossain, A., Pramanick, B., Gitari, H.I., Praharaj, S. et al. (2022). Role of legumes in cropping system for soil ecosystem improvement. Ecosystem Services: Types, Management and Benefits. Nova Science Publishers, Inc, 415.

  13. Kumar, R.B., Dileep, B., Sairam, C. and Hemanth, K. (2022). Incorporating MobileNet models into the classification of bean leaf diseases. Ijfans International Journal of Food and Nutritional Sciences. 11(12): 9596-9597.

  14. 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.

  15. Lugito, N.P.H., Djuwita, R., Adisasmita, A. and Simadibrata, M. (2022). Blood pressure lowering effect of Lactobacillus- containing probiotic. International Journal of Probiotics and Prebiotics. 17(1): 1-13. https://doi.org/10.37290/ IJPP2641-7197.17:1-13

  16. 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.

  17. 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.

  18. Sahu, P., Chug, A., Singh, A.P., Singh, D. and Singh, R.P. (2021). Deep learning models for beans crop diseases: Classification and visualization techniques. International Journal of Modern Agriculture. 10(1): 796.

  19. Sharma, R., Mittal, M., Gupta, V. and Vasdev, D. (2024). Detection of plant leaf disease using advanced deep learning architectures. International Journal of Information Technology. https://doi.org/10.1007/s41870-024-01937-4.

  20. 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.

  21.  
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    Full Research Article

    Detailed Analysis and Advanced Classification of Bean Leaf Diseases using MobileNetV3

    M
    Minakshi C. Satape1
    https://orcid.org/0009-0001-2130-8739
    N
    Namrata A. Jadhav1,*
    https://orcid.org/0009-0003-9378-3103
    D
    Deelip A. Patil1
    https://orcid.org/0000-0002-8302-0888
    V
    Venkata Naga Siva Kumar Challa2
    https://orcid.org/0000-0002-2746-7685
    S
    Shalini Sharma3
    https://orcid.org/0000-0002-9117-6954
    S
    Saba Tahseen4
    https://orcid.org/0000-0001-7185-9312
    1Department of Computer Applications, Kasegaon Education Society’s, Rajarambapu Institute of Technology, affiliated to Shivaji University, Sakharale-415 414, Maharashtra, India.
    2KL Business School, Koneru Lakshmaiah Education Foundation, Guntur-521 180, Andhra Pradesh, India.
    3Department of Language, Culture and Society, SRM Institute of Science and Technology, Delhi NCR Campus, Modinagar, Ghaziabad-201 204, Uttar Pradesh, India.
    4Department of Computer Science and Business Systems, Dayananda Sagar College of Engineering, Bangalore-560 111, Karnataka, India.

    ABSTRACT

    Background: The timely and precise detection of foliage diseases is essential for the efficient management of bean crops. Conventional techniques for detecting leaf diseases need professionals to physically examine the leaves, which is laborious and subject to human mistakes. Due to advanced machine learning (ML) computations, convolutional neural networks (CNNs) are capable of examining leaf diseases. MobileNetV3 is particularly effective for edge device applications because of its lightweight and efficient design.

    Methods: In this study, the effectiveness of MobileNetV3 in distinguishing three disease categories: Angular Leaf Spot, Bacterial Blight and Healthy, from images of bean leaves is evaluated. The dataset utilized in this study is sourced from Kaggle and comprises a total of 1295 images categorized into three classes. Each image was resized to 224´224 pixels and normalized to a range of [0, 1] to prepare it for input into the MobileNetV3 model. Data augmentation techniques, including rotation, flipping and zooming, were applied to enhance the model’s generalization capability. The MobileNetV3 architecture was implemented with a batch size of 32 and trained for 25 epochs using the Adam optimizer with a learning rate of 1e-4. Performance was evaluated based on accuracy, precision, recall and F1-score, using a split dataset consisting of training (80%), validation (10%) and test (10%) subsets.

    Result: The MobileNetV3 model demonstrated a high level of performance in classifying bean leaf diseases. The model achieved an overall accuracy of 92.19% with a macro average F1-score of 92.27%. These results indicate that MobileNetV3 is effective for automated classification of bean leaf diseases, with high precision and recall, particularly for Angular Leaf Spot.

    REFERENCES


    1. Abed, S.H., Al-Waisy, A.S., Mohammed, H.J. and Al-Fahdawi, S. (2021). A modern deep learning framework in robot vision for automated bean leaves diseases detection. International Journal of Intelligent Robotics and Applications. 5(2): 235-51. https://doi.org/10.1007/s41315-021-00174-3.

    2. Abed, S. and Esmaeel, A.A. (2018). A novel approach to classify and detect bean diseases based on image processing. In Proceedings of the 2018 IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE). pp.297-302. IEEE. https://doi.org/10.1109/ISCAIE. 2018. 8405488.

    3. CGIAR Initiative on Foresight. (2023). What do we know about the future of pulses in global and regional agri-food systems? https://www.cgiar.org/news-events/news/what-do- we-know-about-the-future-of-pulses-in-global-and- regional-agri-food-systems/.

    4. Abode, G. (2022). Bean disease dataset [Data set]. Kaggle. https:/ /www.kaggle.com/datasets/therealoise/bean-disease- dataset (Accessed July 25, 2024).

    5. Elfatimi, E., Eryigit, R. and Elfatimi, L. (2022). Beans leaf diseases classification using MobileNet models. IEEE Access. 10: 9471-9482. https://doi.org/10.1109/ACCESS.2022. 3142817.

    6. Food and Agriculture Organization (FAO). (2023). FAOSTAT Database. https://openknowledge.fao.org/server/api/ core/bitstreams/28cfd24e-81a9-4ebc-b2b5-4095fe 5b1dab/content/cc8166en.html [Accessed ON 25/07/ 2024].

    7. Gomez, D., Selvaraj, M.G., Casas, J., Mathiyazhagan, K., Rodriguez, M., Assefa, T., Mlaki, A., Nyakunga, G., Kato, F., Mukankusi, C., Girma, E., Mosquera, G., Arredondo, V. and Espitia, E. (2024). Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI. Scientific Reports. 14(1). https://doi.org/10.1038/s41598-024-66281-w.

    8. Gill, K.S., Anand, V., Gupta, R. and Pahwa, V. (2023). Bean leaf disease classification and visualization using deep learning techniques on sequential model. In Proceedings of the 2023 International Conference in Advances in Power, Signal and Information Technology (APSIT). pp. 14-18. IEEE. https://doi.org/10.1109/APSIT58554.2023. 10201712.

    9. Hazra, K.K., Nath, C.P., Ghosh, P.K. and Swain, D.K. (2020). Inclusion of legumes in rice-wheat cropping system for enhancing carbon sequestration. Carbon management in tropical and sub-tropical terrestrial systems. 23-36.

    10. Heuzé, V., Tran, G., Delagarde, R., Lessire, M. and Lebas, F. (2021). Faba bean (Vicia faba). Feedipedia, a programme by INRAE, CIRAD, AFZ and FAO. Retrieved from https:// www.feedipedia.org/node/4926.

    11. International Food Policy Research Institute (IFPRI). (2022). Global Food Policy Report: Climate Change and Food Systems. https://www.ifpri.org/?s=bean

    12. Jena, J., Maitra, S., Hossain, A., Pramanick, B., Gitari, H.I., Praharaj, S. et al. (2022). Role of legumes in cropping system for soil ecosystem improvement. Ecosystem Services: Types, Management and Benefits. Nova Science Publishers, Inc, 415.

    13. Kumar, R.B., Dileep, B., Sairam, C. and Hemanth, K. (2022). Incorporating MobileNet models into the classification of bean leaf diseases. Ijfans International Journal of Food and Nutritional Sciences. 11(12): 9596-9597.

    14. 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.

    15. Lugito, N.P.H., Djuwita, R., Adisasmita, A. and Simadibrata, M. (2022). Blood pressure lowering effect of Lactobacillus- containing probiotic. International Journal of Probiotics and Prebiotics. 17(1): 1-13. https://doi.org/10.37290/ IJPP2641-7197.17:1-13

    16. 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.

    17. 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.

    18. Sahu, P., Chug, A., Singh, A.P., Singh, D. and Singh, R.P. (2021). Deep learning models for beans crop diseases: Classification and visualization techniques. International Journal of Modern Agriculture. 10(1): 796.

    19. Sharma, R., Mittal, M., Gupta, V. and Vasdev, D. (2024). Detection of plant leaf disease using advanced deep learning architectures. International Journal of Information Technology. https://doi.org/10.1007/s41870-024-01937-4.

    20. 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.

    21.  
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