An Approach for Diagnosing and Differentiating Mango Fruit Diseases Using Hybrid CNN+SVM Classifier
An Approach for Diagnosing and Differentiating Mango Fruit Diseases Using Hybrid CNN+SVM Classifier |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-1 |
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| Year of Publication : 2025 | ||
| Author : Sandeep Kumar, Bhupesh Gupta, Neha Bhatia, K. Anuradha |
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| DOI : 10.14445/22315381/IJETT-V73I1P112 | ||
How to Cite?
Sandeep Kumar, Bhupesh Gupta, Neha Bhatia, K. Anuradha, "An Approach for Diagnosing and Differentiating Mango Fruit Diseases Using Hybrid CNN+SVM Classifier," International Journal of Engineering Trends and Technology, vol. 73, no. 1, pp. 146-154, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I1P112
Abstract
Fruits are an important source of nutrients needed to maintain excellent human health. The major cause of the notable decrease in crop yield is the considerable influence of fruit diseases, which result from poor maintenance techniques and the spread of fungal infections. Mango fruit is consumed widely around the world, although its quantity and quality might be affected by diseases. The laborious, time-consuming, heavily dependent on human labor, and inefficient aspects of the manual inspection process define it. The goal of this work is to create a hybrid model for mango fruit sickness detection that combines the strengths of a support vector machine and a resilient convolutional neural network. The salient features of both classifiers are combined in the study. In the proposed hybrid model, CNN serves as an automated feature extractor and SVM as a binary classifier. The suggested model's algorithm is trained and assessed using this dataset, which features images of four distinct mango fruit diseases and healthy mangoes. The findings show the efficacy of the suggested work with a 99% detection accuracy over the mango fruit disease dataset.
Keywords
CNN, SVM, Mango Fruit Disease, Diagnosing, Differentiating.
References
[1] Charles Samuel Mutengwa, Pearson Mnkeni, and Aleck Kondwakwenda, “Climate-Smart Agriculture and Food Security in Southern Africa: A Review of the Vulnerability of Smallholder Agriculture and Food Security to Climate Change,” Sustainability, vol. 15, no. 4, pp. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Subhasis Bhadra, and Allen R. Dyer, Resilience and Well-Being among the Survivors of Natural Disasters and Conflicts, Handbook of Health and Well-Being, Springer Nature, pp. 637-667, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Sulogna Chakma et al., “Adapting Land Degradation and Enhancing Ethnic Livelihood Security through Fruit Production: Evidence from Hilly Areas of Bangladesh,” Agro-Biodiversity and Agri-Ecosystem Management, Singapore: Springer, pp. 217-238, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] V. Laxmi, and R. Roopalakshmi, “Artificially Ripened Mango Fruit Prediction System Using Convolutional Neural Network,” Intelligent Systems and Sustainable Computing: Intelligent Systems and Sustainable Computing, Singapore, pp. 345-356, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Rabia Saleem et al., “Mango Leaf Disease Identification Using Fully Resolution Convolutional Network,” Computers, Materials and Continua, vol. 69, no. 3, pp. 3581-3601, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] R. Nithya et al., “Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network,” Foods, vol. 11, no. 21, pp. 1-11, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Omneya Attallah, “Tomato Leaf Disease Classification via Compact Convolutional Neural Networks with Transfer Learning and Feature Selection,” Horticulturae, vol. 9, no. 2, pp. 1-19, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Anil Bhujel et al., “A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification,” Agriculture, vol. 12, no. 2, pp. 1-18, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Prabhjot Kaur et al., “Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction,” Sensors, vol. 22, no. 2, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Umit Atila et al., “Plant Leaf Disease Classification Using Efficient Net Deep Learning Model,” Ecological Informatics, vol. 61, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Sunayana Arya, and Rajeev Singh, “A Comparative Study of CNN and AlexNetfor Detection of Disease in Potato and Mango Leaf,” International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), Ghaziabad, India, pp. 1-6, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] S. Wongsila, P. Chantrasri, and P. Sureephong, “Machine Learning Algorithm Development for Detection of Mango Infected by Anthracnose Disease,” Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, Cha-am, Thailand, pp. 249-252, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Soumia Bensaadi, and Ahmed Louchene, “Low-Cost Convolutional Neural Network for Tomato Plant Diseases Classifiation,” IAES International Journal of Artificial Intelligence, vol. 12, no. 1, pp. 162-170, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Rabbia Mahum et al., “A Novel Framework for Potato Leaf Disease Detection Using an Efficient Deep Learning Model,” Human and Ecological Risk Assessment: An International Journal, vol. 29, no. 2, pp. 303-326, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Jinzhu Lu, Lijuan Tan, and Huanyu Jiang, “Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification,” Agriculture, vol. 11, no. 8, pp. 1-18, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Uday Pratap Singh et al., “Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease,” IEEE Access, vol. 7, pp. 43721-43729, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Maha Altalak, “Smart Agriculture Applications Using Deep Learning Technologies: A Survey,” Applied Sciences, vol. 12, no. 12, pp. 1-20, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Vani Ashok, and D.S. Vinod, “A Comparative Study of Feature Extraction Methods in Defect Classification of Mangoes Using Neural Network,” 2nd International Conference on Cognitive Computing and Information Processing, pp. 1-6, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Siddharth Singh Chouhan, Uday Pratap Singh, and Sanjeev Jain, “Web Facilitated Anthracnose Disease Segmentation from the Leaf of Mango Tree Using Radial Basis Function (RBF) Neural Network,” Wireless Personal Communcations, 113, pp. 1279-1296, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Aayush Sharma, Harjeet Kaur, and Deepak Prashar, “Generative Adversarial Networks-Based Approach For Data Augmentation In Mango Leaf Disease Detection System,” International Conference on Communication Systems and Network Technologies (CSNT), India, pp. 816-821, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Byeongjun Min et al., “Data Augmentation Method for Plant Leaf Disease Recognition,” Applied Sciences, vol. 13, no. 3, pp. 1-17, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Ramalingam Kalaivani, and Arunachalam Saravanan, “A CONV-EGBDNN Model for the Classification and Detection of Mango Diseases on Diseased Mango Images utilizing Transfer Learning,” Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 14349-14354, 2024.
[CrossRef] [Google Scholar] [Publisher Link]