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Current IssueSpecial IssueEditorial BoardOnline First ArticlesArchive

Full Research Article

Deep Learning Framework for the Automated Identification and Classification of Mango Disease, Pest and Healthy Conditions

P
Prashant Chaudhary1,*
https://orcid.org/0000-0001-5221-6274
T
Taniya Sengupta Rathore2
https://orcid.org/0000-0002-9987-1871
D
Durgaprasad Navulla3
https://orcid.org/0000-0001-5942-6864
B
Bharati Chandrayan4
https://orcid.org/0000-0002-5691-136X
K
Kunal D. Jadhav5
https://orcid.org/0000-0001-8331-5363
A
Alok S. Shah6
https://orcid.org/0000-0001-5708-3546
1Department of Information Technology, Usha Pravin Gandhi College of Arts, Science and Commerce, SVKM, Mumbai-400 056, Maharashtra, India.
2Department of Biotechnology, School of Science, O.P. Jindal University, Raigarh-49 6109, Chhattisgarh, India.
3KL Business School, Koneru Lakshmaiah Education Foundation, Guntur-521 180 andhra Pradesh, India.
4Department of Language, Culture and Society, SRM Institute of Science and Technology Delhi NCR Campus, Ghaziabad-201 204, Uttar Pradesh, India.
5Department of Lifelong Learning and Extension, University of Mumbai, Mumbai-400 020, Maharashtra, India.
6Bharati Vidyapeeth (Deemed to be) University, Department of Management Studies, Kharghar, Navi Mumbai-410 210, Maharashtra, India.

ABSTRACT

Background: Mango (Mangifera indica) is a commercially important fruit crop, but its yield and quality are often threatened by diseases and insect pests. Among the most common are fungal diseases such as sooty mould and powdery mildew and insect pests like gall midge. If not detected early, these cause substantial economic losses to farmers. Manual identification is time-consuming and error-prone, creating the need for automated solutions. Deep learning, particularly Convolutional Neural Networks (CNNs), has shown strong potential in plant disease and pest recognition.
Methods: In this study, a sequential CNN-based framework was developed for the automated identification and classification of mango leaf images into four classes: Sooty mould, powdery mildew, gall midge and healthy. The architecture consisted of five convolutional layers with max-pooling for feature extraction, followed by fully connected layers for classification. Model performance was assessed using accuracy, precision, recall, F1-score and confusion matrix analysis.
Result: The model achieved an overall accuracy of 96.0%, with high weighted average precision, recall and F1-scores, indicating reliable performance despite class imbalance. The confusion matrix confirmed the model’s capability to distinguish between disease, pest and healthy conditions with minimal misclassification.

REFERENCES


  1. Adeleye, O.A., Bamiro, O.A., Bakre, L.G., Odeleye, F.O., Adebowale, M.N., Okunye, O.L., Sodeinde, M.A., Adebona, A.C. and Menaa, F. (2021). Medicinal plants with potential inhibitory bioactive compounds against coronaviruses. Advanced Pharmaceutical Bulletin. https://doi.org/10.34172/ apb.2022.003.

  2. Ali, S., Ibrahim, M. Ahmed, S.I., Nadim, M., Mizanur, M.R., Shejunti, M.M. and Jabid, T.  (2022). MangoLeafBD dataset. Mendeley Data. V1, doi: 10.17632/hxsnvwty3r.1.

  3. AlZubi, A.A. (2023). Artificial intelligence and its application in the prediction and diagnosis of animal diseases: A review. Indian Journal of Animal Research. 57(10): 1265-1271. doi: 10.18805/IJAR.BF-1684. 

  4. Charte, D., Charte, F. and Herrera, F. (2021). Reducing data complexity using autoencoders with class-informed loss functions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(12): 9549-9560. https://doi.org/10.1109/ TPAMI.2021.3127698.

  5. Cho, O.H. (2024). An evaluation of various machine learning approaches for detecting leaf diseases in agriculture. Legume Research. 47(4): 619-627. doi: 10.18805/LRF- 787.

  6. Food and Agriculture Organisation (FAO). 2023. Global Report on Food Crises: Number of People Facing Acute Food Insecurity Rose to 258 Million in 58 Countries in 2022. https://www.fao.org/newsroom/detail/global-report-on- food-crises-GRFC-2023-GNAFC-fao-wfp-unicef-ifpri/ en#:~:text =The%20 report%20 finds%20that%2 0around, year% 20history%20of%20the%20report. [Accessed on 11/05/2023].

  7. Gautam, V., Ranjan, R.K., Dahiya, P. and Kumar, A. (2023). ESDNN: A novel ensembled stack deep neural network for mango leaf disease classification and detection. Multimedia Tools and Applications. 83(4): 10989-11015. https:// doi.org/10.1007/s11042-023-16012-6.

  8. Gulavnai, S. and Patil, R. (2019). Deep learning for image based mango leaf disease detection. International Journal of Recent Technology and Engineering. 8(3S3): 54-56.

  9. https://www.mango.org/wp-content/uploads/2020/08/Mango_ Pests_and_ Diseases_ENG.

  10. Hussain, S.Z., Naseer, B., Qadri, T., Fatima, T. and Bhat, T.A. (2021). Mango (Mangifera indica)-Morphology, Taxonomy, Composition and Health Benefits. In Fruits Grown in Highland Regions of the Himalayas: Nutritional and Health Benefits. Cham: Springer International Publishing. (pp. 245-255).

  11. Iqbal, N.S., Atiq, N.M., Fayyaz, N.M., Zakria, N.M., Rajput, N.N.A., Aasma, N., Kachelo, N.G. A., Ahmad, N.I., Usman, N.M. and Mehmood, N.A. (2024). Powdery mildew of mango current status, prospective and emerging tools for management. Agricultural Sciences Journal. 6(1): 92- 101. https://doi.org/10.56520/asj.v6i1.365.

  12. Kalaivani, R. and Saravanan, A. (2024). A CONV-EGBDNN model for the classification and detection of mango diseases on diseased mango images utilizing transfer learning. Engineering, Technology and Applied Science Research.  14(3): 14349-14354.

  13. Khan, A.U., Choudhury, M.A.R., Tarapder, S.A., Maukeeb, A.R.M. and Ema, I.J. (2020). Status of mango fruit infestation at home garden in Mymensingh, Bangladesh. Curr. Rese. Agri. Far.   1(4): 35-42. http://dx.doi.org/10.18782/2582- 7146.119. 

  14. Kusuma, K.B.M., Arora, M.,  AlZubi, A.A., Verma, A. and Andrze, S. (2022). Application of blockchain and internet of things (IoT) in the food and beverage industry. Pacific Business Review (International). 15(10): 50-59.

  15. Mia, M.R., Roy, S., Das, S.K. and Rahman, M.A. (2020). Mango leaf disease recognition using neural network and support vector machine. Iran Journal of Computer Science. 3(3): 185-193. https://doi.org/10.1007/s42044-020-00057-z. 

  16. Mustafa, S.K., Oyouni, A.A.W.A., Aljohani, M. and Ahmad, M.A. (2020). Polyphenols more than an antioxidant: Role and scope. Journal of Pure and Applied Microbiology. 14(1): 47-61.

  17. Nalawade, R.R., Sawant, S.D., Joshi, M.S., Ingle, P.M., More, V.G. and Kadam, J.J. (2023). Mango anthracnose and powdery mildew disease detection using convolutional neural network and artificial neural network. Journal of Plant Disease Sciences. 18(1): 11-19. doi: https://doi.org./ 10.48165/jpds.2023.1801.03. 

  18. Noce, A., Romani, A. and Bernini, R. (2021). Dietary intake and chronic disease prevention. Nutrients. 13(4): 1358.

  19. Pratap, V.K. and Kumar, N.S. (2024). Deep learning based mango leaf disease detection for classifying and evaluating mango leaf diseases. Fusion: Practice and Applications. 15(2): 261-61. doi: https://doi.org/10.54216/FPA.150222. 

  20. Rajan, S., Srivastav, M. and Rymbai, H. (2021). Genetic Resources in Mango. In: The Mango Genome. [Kole, C. (Ed.)], Springer. (pp. 63-88). https://doi.org/10.1007/978-3-030- 47829-2_4.

  21. Rajbongshi, A., Khan, T., Pramanik, M.M.R.A., Tanvir, S.M. and Siddiquee, N.R.C. (2021). Recognition of mango leaf disease using convolutional neural network models: A transfer learning approach. Indonesian Journal of Electrical Engineering and Computer Science. 23(3): 1681-1688. doi: 10.11591/ijeecs.v23.i3.pp1681-1688.  

  22. Rao, U.S., Swathi, R., Sanjana, V., Arpitha, L., Chandrasekhar, K. and Naik, P.K. (2021). Deep learning precision farming: Grapes and mango leaf disease detection by transfer learning. Global Transitions Proceedings. 2(2): 535- 544. https://doi.org/10.1016/j.gltp.2021.08.002.

  23. Reddy, P.V.R., Rashmi, M.A., Sreedevi, K. and Singh, S. (2020). Sucking pests of mango. Sucking Pests of Crops. pp. 411-424.

  24. Rizvee, R.A., Orpa, T.H., Ahnaf, A., Kabir, M.A., Rashid, M.R.A., Islam, M.M., Islam, M., Jabid, T. and Ali, M.S. (2023). LeafNet: A proficient convolutional neural network for detecting seven prominent mango leaf diseases. Journal of Agriculture and Food Research. 14: 100787. https://doi.org/10.1016/ j.jafr.2023.100787.

  25. Sammut, C. and Webb, G.I. (Eds.). (2011). Encyclopedia of Machine Learning. Springer Science and Business Media. (pp. 292-293).

  26. Sharma, A., Bijral, R.K., Manhas, J. and Sharma, V. (2022). Mango leaf diseases detection using deep learning. International Journal of Knowledge Based Computer Systems. 10(1): 40-44.

  27. Shickel, B. and Rashidi, P. (2020). Sequential interpretability: Methods, applications and future direction for understanding deep learning models in the context of sequential data. arXiv preprint arXiv. 2004.12524.

  28. Singh, Y., Sinha, S., Malvi, S., Karte, S. and Mimrot, M.K. (2022). Importance disease and pest of mango crop and their management. Advances in Horticulture Crops and Their Challenges. pp 103-128.

  29. Vijay, C.P. and Pushpalatha, K. (2023). Revolutionizing mango leaf disease detection: Leveraging segmentation and hybrid deep learning for enhanced accuracy and sustainability. International Journal of Intelligent Systems and Applications in Engineering. 11(4): 121-131.

  30. Wasik, S. and Pattinson, R.  (2024). Artificial intelligence applications in fish classification and taxonomy: Advancing our understanding of aquatic biodiversity. Fish Taxa. 31: 11-21. 

  31. Yang, S. and Berdine, G. (2017). The receiver operating characteristic (ROC) curve. The Southwest Respiratory and Critical Care Chronicles. 5(19): 34-36.

  32. Zock, M., Chersoni, E., Hsu, Y.Y. and De Deyne, S. (2024). The Workshop on Cognitive Aspects of the Lexicon (CogALex@  LREC-COLING 2024). In the 8th Workshop on Cognitive Aspects of the Lexicon. European Language Resources Association.
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    Full Research Article

    Deep Learning Framework for the Automated Identification and Classification of Mango Disease, Pest and Healthy Conditions

    P
    Prashant Chaudhary1,*
    https://orcid.org/0000-0001-5221-6274
    T
    Taniya Sengupta Rathore2
    https://orcid.org/0000-0002-9987-1871
    D
    Durgaprasad Navulla3
    https://orcid.org/0000-0001-5942-6864
    B
    Bharati Chandrayan4
    https://orcid.org/0000-0002-5691-136X
    K
    Kunal D. Jadhav5
    https://orcid.org/0000-0001-8331-5363
    A
    Alok S. Shah6
    https://orcid.org/0000-0001-5708-3546
    1Department of Information Technology, Usha Pravin Gandhi College of Arts, Science and Commerce, SVKM, Mumbai-400 056, Maharashtra, India.
    2Department of Biotechnology, School of Science, O.P. Jindal University, Raigarh-49 6109, Chhattisgarh, India.
    3KL Business School, Koneru Lakshmaiah Education Foundation, Guntur-521 180 andhra Pradesh, India.
    4Department of Language, Culture and Society, SRM Institute of Science and Technology Delhi NCR Campus, Ghaziabad-201 204, Uttar Pradesh, India.
    5Department of Lifelong Learning and Extension, University of Mumbai, Mumbai-400 020, Maharashtra, India.
    6Bharati Vidyapeeth (Deemed to be) University, Department of Management Studies, Kharghar, Navi Mumbai-410 210, Maharashtra, India.

    ABSTRACT

    Background: Mango (Mangifera indica) is a commercially important fruit crop, but its yield and quality are often threatened by diseases and insect pests. Among the most common are fungal diseases such as sooty mould and powdery mildew and insect pests like gall midge. If not detected early, these cause substantial economic losses to farmers. Manual identification is time-consuming and error-prone, creating the need for automated solutions. Deep learning, particularly Convolutional Neural Networks (CNNs), has shown strong potential in plant disease and pest recognition.
    Methods: In this study, a sequential CNN-based framework was developed for the automated identification and classification of mango leaf images into four classes: Sooty mould, powdery mildew, gall midge and healthy. The architecture consisted of five convolutional layers with max-pooling for feature extraction, followed by fully connected layers for classification. Model performance was assessed using accuracy, precision, recall, F1-score and confusion matrix analysis.
    Result: The model achieved an overall accuracy of 96.0%, with high weighted average precision, recall and F1-scores, indicating reliable performance despite class imbalance. The confusion matrix confirmed the model’s capability to distinguish between disease, pest and healthy conditions with minimal misclassification.

    REFERENCES


    1. Adeleye, O.A., Bamiro, O.A., Bakre, L.G., Odeleye, F.O., Adebowale, M.N., Okunye, O.L., Sodeinde, M.A., Adebona, A.C. and Menaa, F. (2021). Medicinal plants with potential inhibitory bioactive compounds against coronaviruses. Advanced Pharmaceutical Bulletin. https://doi.org/10.34172/ apb.2022.003.

    2. Ali, S., Ibrahim, M. Ahmed, S.I., Nadim, M., Mizanur, M.R., Shejunti, M.M. and Jabid, T.  (2022). MangoLeafBD dataset. Mendeley Data. V1, doi: 10.17632/hxsnvwty3r.1.

    3. AlZubi, A.A. (2023). Artificial intelligence and its application in the prediction and diagnosis of animal diseases: A review. Indian Journal of Animal Research. 57(10): 1265-1271. doi: 10.18805/IJAR.BF-1684. 

    4. Charte, D., Charte, F. and Herrera, F. (2021). Reducing data complexity using autoencoders with class-informed loss functions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(12): 9549-9560. https://doi.org/10.1109/ TPAMI.2021.3127698.

    5. Cho, O.H. (2024). An evaluation of various machine learning approaches for detecting leaf diseases in agriculture. Legume Research. 47(4): 619-627. doi: 10.18805/LRF- 787.

    6. Food and Agriculture Organisation (FAO). 2023. Global Report on Food Crises: Number of People Facing Acute Food Insecurity Rose to 258 Million in 58 Countries in 2022. https://www.fao.org/newsroom/detail/global-report-on- food-crises-GRFC-2023-GNAFC-fao-wfp-unicef-ifpri/ en#:~:text =The%20 report%20 finds%20that%2 0around, year% 20history%20of%20the%20report. [Accessed on 11/05/2023].

    7. Gautam, V., Ranjan, R.K., Dahiya, P. and Kumar, A. (2023). ESDNN: A novel ensembled stack deep neural network for mango leaf disease classification and detection. Multimedia Tools and Applications. 83(4): 10989-11015. https:// doi.org/10.1007/s11042-023-16012-6.

    8. Gulavnai, S. and Patil, R. (2019). Deep learning for image based mango leaf disease detection. International Journal of Recent Technology and Engineering. 8(3S3): 54-56.

    9. https://www.mango.org/wp-content/uploads/2020/08/Mango_ Pests_and_ Diseases_ENG.

    10. Hussain, S.Z., Naseer, B., Qadri, T., Fatima, T. and Bhat, T.A. (2021). Mango (Mangifera indica)-Morphology, Taxonomy, Composition and Health Benefits. In Fruits Grown in Highland Regions of the Himalayas: Nutritional and Health Benefits. Cham: Springer International Publishing. (pp. 245-255).

    11. Iqbal, N.S., Atiq, N.M., Fayyaz, N.M., Zakria, N.M., Rajput, N.N.A., Aasma, N., Kachelo, N.G. A., Ahmad, N.I., Usman, N.M. and Mehmood, N.A. (2024). Powdery mildew of mango current status, prospective and emerging tools for management. Agricultural Sciences Journal. 6(1): 92- 101. https://doi.org/10.56520/asj.v6i1.365.

    12. Kalaivani, R. and Saravanan, A. (2024). A CONV-EGBDNN model for the classification and detection of mango diseases on diseased mango images utilizing transfer learning. Engineering, Technology and Applied Science Research.  14(3): 14349-14354.

    13. Khan, A.U., Choudhury, M.A.R., Tarapder, S.A., Maukeeb, A.R.M. and Ema, I.J. (2020). Status of mango fruit infestation at home garden in Mymensingh, Bangladesh. Curr. Rese. Agri. Far.   1(4): 35-42. http://dx.doi.org/10.18782/2582- 7146.119. 

    14. Kusuma, K.B.M., Arora, M.,  AlZubi, A.A., Verma, A. and Andrze, S. (2022). Application of blockchain and internet of things (IoT) in the food and beverage industry. Pacific Business Review (International). 15(10): 50-59.

    15. Mia, M.R., Roy, S., Das, S.K. and Rahman, M.A. (2020). Mango leaf disease recognition using neural network and support vector machine. Iran Journal of Computer Science. 3(3): 185-193. https://doi.org/10.1007/s42044-020-00057-z. 

    16. Mustafa, S.K., Oyouni, A.A.W.A., Aljohani, M. and Ahmad, M.A. (2020). Polyphenols more than an antioxidant: Role and scope. Journal of Pure and Applied Microbiology. 14(1): 47-61.

    17. Nalawade, R.R., Sawant, S.D., Joshi, M.S., Ingle, P.M., More, V.G. and Kadam, J.J. (2023). Mango anthracnose and powdery mildew disease detection using convolutional neural network and artificial neural network. Journal of Plant Disease Sciences. 18(1): 11-19. doi: https://doi.org./ 10.48165/jpds.2023.1801.03. 

    18. Noce, A., Romani, A. and Bernini, R. (2021). Dietary intake and chronic disease prevention. Nutrients. 13(4): 1358.

    19. Pratap, V.K. and Kumar, N.S. (2024). Deep learning based mango leaf disease detection for classifying and evaluating mango leaf diseases. Fusion: Practice and Applications. 15(2): 261-61. doi: https://doi.org/10.54216/FPA.150222. 

    20. Rajan, S., Srivastav, M. and Rymbai, H. (2021). Genetic Resources in Mango. In: The Mango Genome. [Kole, C. (Ed.)], Springer. (pp. 63-88). https://doi.org/10.1007/978-3-030- 47829-2_4.

    21. Rajbongshi, A., Khan, T., Pramanik, M.M.R.A., Tanvir, S.M. and Siddiquee, N.R.C. (2021). Recognition of mango leaf disease using convolutional neural network models: A transfer learning approach. Indonesian Journal of Electrical Engineering and Computer Science. 23(3): 1681-1688. doi: 10.11591/ijeecs.v23.i3.pp1681-1688.  

    22. Rao, U.S., Swathi, R., Sanjana, V., Arpitha, L., Chandrasekhar, K. and Naik, P.K. (2021). Deep learning precision farming: Grapes and mango leaf disease detection by transfer learning. Global Transitions Proceedings. 2(2): 535- 544. https://doi.org/10.1016/j.gltp.2021.08.002.

    23. Reddy, P.V.R., Rashmi, M.A., Sreedevi, K. and Singh, S. (2020). Sucking pests of mango. Sucking Pests of Crops. pp. 411-424.

    24. Rizvee, R.A., Orpa, T.H., Ahnaf, A., Kabir, M.A., Rashid, M.R.A., Islam, M.M., Islam, M., Jabid, T. and Ali, M.S. (2023). LeafNet: A proficient convolutional neural network for detecting seven prominent mango leaf diseases. Journal of Agriculture and Food Research. 14: 100787. https://doi.org/10.1016/ j.jafr.2023.100787.

    25. Sammut, C. and Webb, G.I. (Eds.). (2011). Encyclopedia of Machine Learning. Springer Science and Business Media. (pp. 292-293).

    26. Sharma, A., Bijral, R.K., Manhas, J. and Sharma, V. (2022). Mango leaf diseases detection using deep learning. International Journal of Knowledge Based Computer Systems. 10(1): 40-44.

    27. Shickel, B. and Rashidi, P. (2020). Sequential interpretability: Methods, applications and future direction for understanding deep learning models in the context of sequential data. arXiv preprint arXiv. 2004.12524.

    28. Singh, Y., Sinha, S., Malvi, S., Karte, S. and Mimrot, M.K. (2022). Importance disease and pest of mango crop and their management. Advances in Horticulture Crops and Their Challenges. pp 103-128.

    29. Vijay, C.P. and Pushpalatha, K. (2023). Revolutionizing mango leaf disease detection: Leveraging segmentation and hybrid deep learning for enhanced accuracy and sustainability. International Journal of Intelligent Systems and Applications in Engineering. 11(4): 121-131.

    30. Wasik, S. and Pattinson, R.  (2024). Artificial intelligence applications in fish classification and taxonomy: Advancing our understanding of aquatic biodiversity. Fish Taxa. 31: 11-21. 

    31. Yang, S. and Berdine, G. (2017). The receiver operating characteristic (ROC) curve. The Southwest Respiratory and Critical Care Chronicles. 5(19): 34-36.

    32. Zock, M., Chersoni, E., Hsu, Y.Y. and De Deyne, S. (2024). The Workshop on Cognitive Aspects of the Lexicon (CogALex@  LREC-COLING 2024). In the 8th Workshop on Cognitive Aspects of the Lexicon. European Language Resources Association.
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