
Scientific Journal Of King Faisal University: Basic and Applied Sciences
Scientific Journal of King Faisal University: Basic and Applied Sciences
Synergistic Use of Convolutional Neural Networks and Support Vector Machines for Mango Leaf Disease Diagnosis
(Salma N., Madhuri G.R. and Basavaraj Jagadale)Abstract
Even in the age of digitalisation and capitalism, agriculture still plays a significant role in many economies, such as in certain Asian countries where mangoes have become an important export commodity. However, plant diseases put serious constraints on both productivity and quality. Existing methods for identifying disease typically rely on the experience of farmers and are time-consuming and error-prone. In this study, we propose a new hybrid framework consisting of a custom Convolutional Neural Network (CNN) and a Support Vector Machine (SVM) classifier to classify eight mango leaf conditions: Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, Healthy, and Sooty Mould. The model uses a dataset of 4000 images collected from mango orchards throughout Bangladesh and incorporates rigorous pre-processing and data augmentation to help improve model robustness and generalisability. The results indicate that the hybrid CNN-SVM model performs best, outperforming state-of-the-art models with an accuracy of 99.75%. The research thus emphasises the role of deep learning and machine learning in enabling more accurate disease detection in agriculture, benefitting farms and the environment via sustainable practices and higher crop yields.
KEYWORDS
Classification, custom CNN model, deep learning, disease detection, feature extraction, machine learning
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References
Ahmed, S.I., Ibrahim, M., Nadim, M., Rahman, M.M., Shejunti, M.M., Jabid, T. and Ali, M.S. (2023). MangoLeafBD: A comprehensive image dataset to classify diseased and healthy mango leaves. Data in Brief, 47(n/a), 108941. DOI: 10.1016/j.dib.2023.108941.
Gautam, V. and Rani, J. (2022). Mango leaf stress identification using deep neural network. Intelligent Automation and Soft Computing, 34(2), 849–64. DOI: 10.32604/iasc.2022.025113.
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), 10989–1015. DOI: 10.1007/s11042-023-16012-6.
Gupta, P. and Mukhopadhyay, S. (2022). Efficient estimation of empirical Green’s function by removing transients from sensor data using time–frequency normalization technique. IEEE Sensors Journal, 22(24), 24344–51. DOI: 10.1109/JSEN.2022.3221947.
Hossain, M.A., Sakib, S., Abdullah, H.M. and Arman, S.E. (2024). Deep learning for mango leaf disease identification: A vision transformer perspective. Heliyon, 10(17), e36361. DOI: 10.1016/j.heliyon.2024.e36361.
Jain, S. and Jaidka, P. (2023). Mango leaf disease classification using deep learning hybrid model. In: 2023 International Conference on Power, Instrumentation, Energy and Control (PIECON). IEEE, Aligarh, India, 10-12/02/2023 DOI: 10.1109/PIECON56912.2023.10085869.
Jiang, Z., Li, R., Tang, Y., Cheng, Z., Qian, M., Li, W. and Shao, Y. (2022). Transcriptome analysis reveals the inducing effect of Bacillus siamensis on disease resistance in postharvest mango fruit. Foods, 11(1), 107. DOI: 10.3390/foods11010107.
Karthik, R., Hariharan, M. and Menaka, R. (2020). Attention embedded residual CNN for disease detection in tomato leaves. Applied Soft Computing, 86(n/a), 105933. DOI: 10.1016/j.asoc.2019.105933.
Kaur, R., Gabrijelčič, D. and Klobučar, T. (2023). Artificial intelligence for cybersecurity: Literature review and future research directions. Information Fusion, 97(n/a), 101804. DOI: 10.1016/j.inffus.2023.101804.
Koushik, H.A., Bharadwaj, R.B., Naik, R.P.E., Ramesh, G., Yogesh, M.J. and Habeeb, S. (2020). Detection and classification of diseased mangoes. In: 2020 International Conference on Computer Science and Its Application in Agriculture (ICOSICA). IEEE, Bogor, Indonesia, 16-17/09/2020. DOI: 10.1109/ICOSICA49951.2020.
Kumar, P., Ashtekar, S., Jayakrishna, S.S., Bharath, K.P., Vanathi, P.T. and Kumar, M.R. (2021). Classification of mango leaves infected by fungal disease anthracnose using deep learning. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE. Erode, India, 8–10/04/2021. DOI: 10.1109/ICCMC51019.2021.
Limsripraphan, W. and Yammen, S. (2024). A novel algorithm for classifying textile fiber using the proposed three-class support vector machine with matched filters. GMSARN International Journal. 18(n/a), 392–400.
Maheshwari, K., Choure, P.K. and Birchha, V. (2021). Performance analysis of mango leaf disease using machine learning technique. International Journal for Research in Applied Science and Engineering Technology, 9(1), 856–62. DOI: 10.22214/ijraset.2021.32926.
Mathur, P., Sheth, F., Goyal, D. and Gupta, A. (2024). Deep insight: Mathematical modeling and statistical analysis for mango leaf disease classification using advanced deep learning models. Journal of Interdisciplinary Mathematics, 27(2), 317–42. DOI: 10.47974/jim-1830.
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), 185–93. DOI: 10.1007/s42044-020-00057-z.
Mohapatra, M., Parida, A.K., Mallick, P.K., Zymbler, M. and Kumar, S. (2022). Botanical leaf disease detection and classification using convolutional neural network: a hybrid metaheuristic enabled approach. Computers, 11(5), 82. DOI: 10.3390/computers11050082.
Nayef, M.A. and Al-Barhawee, N.I.K. (2025). Novel cell-free suspensions of symbiotic bacteria for biocontrol of phytopathogenic bacteria. Scientific Journal of King Faisal University: Basic and Applied Sciences, 26(1), 57–63. DOI: 10.37575/b/sci/250009.
Nishat, M.M. and Faisal, F. (2018). An Investigation of Spectroscopic Characterization on Biological Tissue. In 2018 4th International Conference on Electrical Engineering and Information and Communication Technology (iCEEiCT). IEEE. Dhaka, Bangladesh,13–15 /09/2018. DOI: 10.1109/iCEEiCT45139.2018.
Parashar, N., Yadav, R. and Srivastava, K. (2023). Fruits variety identification using VGG-16 approach. In: 2nd International Conference on Futuristic and Sustainable Aspects in Engineering and Technology: Fsaet-2021. AIP Publishing LLC, Mathura, India, 24-26/12/2021. DOI: 10.1063/5.0153934.
Patel, A., Mishra, R. and Sharma, A. (2023). Maize plant leaf disease classification using supervised machine learning algorithms. Fusion: Practice and Applications, 13(2), 08–21. DOI: 10.54216/FPA.130201.
Patil, R.Y., Gulvani, S., Waghmare, V.B. and Mujawar, I.K. (2022). Image based anthracnose and red-rust leaf disease detection using deep learning. TELKOMNIKA (Telecommunication Computing Electronics and Control), 20(6), 1256–63. DOI: 10.12928/telkomnika.v20i6.24262.
Pham, T.N., Van Tran, L. and Dao, S.V.T. (2020). Early disease classification of mango leaves using feed-forward neural network and hybrid metaheuristic feature selection. IEEE access, 8(n/a), 189960–73. DOI: 10.1109/access.2020.3031914.
Porna, S.B., Kabir, M.F., Rana, M.I.C., Sajol, M.S.I., Roy, T., Khan, M.A.U. and Bhavani, G.D. (2024). Hybrid Convolutional Neural Networks for Enhanced Detection of Mango Leaf Diseases. In: 2024 IEEE 6th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA). IEEE. 19-20/10/2024. DOI: 10.1109/ICCCMLA63077.2024.
Prabu, M. and Chelliah, B.J. (2022). Mango leaf disease identification and classification using a CNN architecture optimized by crossover-based levy flight distribution algorithm. Neural Computing and Applications, 34(9), 7311–24. DOI: 10.1007/s00521-021-06726-9.
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), 261–77. DOI: 10.54216/FPA.150222.
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), 1681–8. DOI: 10.11591/ijeecs.v23.i3.pp1681-1688.
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), 535–44. DOI: 10.1016/j.gltp.2021.08.002.
Saleem, R., Shah, J.H., Sharif, M., Yasmin, M., Yong, H.S. and Cha, J. (2021). Mango leaf disease recognition and classification using novel segmentation and vein pattern technique. Applied Sciences, 11(24), 11901. DOI: 10.3390/app112411901.
Salma, N. and Madhuri, G.R. (2024). Advancing remote sensing: a unified deep learning approach with pretrained and custom architectures for high-precision classification. Physica Scripta, 99(11), 116012. DOI: 10.1088/1402-4896/ad8491.
Salma, N., Madhuri, G.R. and Akshata, G.M. (2024). Robust brain tumor detection and classification via multi-technique image analysis. Physica Scripta, 99(7), 076020. DOI: 10.1088/1402-4896/ad591b.
Sharma, R., Kukreja, V. and Vats, S. (2023). A new dawn for tomato-spotted wilt virus detection and intensity classification: A CNN and LSTM ensemble model. In: 2023 4th International Conference for Emerging Technology (INCET). IEEE, Belgaum, India, 26-28/5/2023. DOI: 10.1109/INCET57972.2023.10170406.
Ullagaddi, S.B. and Raju, S.V. (2017). Disease recognition in Mango crop using modified rotational kernel transform features. In: 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE. Coimbatore, India, 6–7/01/2017. DOI: 10.1109/ICACCS40226.2017.
Yuliansyah, H., Hartanto, R. and Soesanti, I. (2021). Evaluation of the deep learning techniques to identify plant diseases using leaf images. International Journal on Electrical Engineering and Informatics, 13(4), 1–11. DOI: 10.15676/ijeei.2021.13.4.5.