Mango leaf disease detection using deep feature extraction and machine learning methods: A comparative survey

Yıl 2025, Cilt: 12 Sayı: 1, 35 - 43, 31.01.2025

Yavuz Ünal , Muammer Türkoğlu

Öz

Plant diseases significantly affect the quality and quantity of agricultural production. Diseases seen in the leaves of plants adversely affect plant growth and yield. In the near future, accessing cheap and safe food will be one of the most important problems of countries. Therefore, early detection of plant diseases is very important in terms of economy and access to food. It is very difficult to visually detect and monitor the diseases in mango leaves. This study aims to detect diseases in mango leaves with the aid of image processing and deep learning. Deep features are extracted from mango leaf images (by using Darknet19, Xception, SqueezeNet, MobileNetv2, DenseNet201, GoogleNet, ResNet18, VGG16 and AlexNet architectures) and classified with Decision Tree, Linear Discriminant Analysis, Naive Bayes, Support Vector Machine, k-Nearest Neighbors, Ensemble Classifier. As the results of the evaluations, it is observed that the results found in the literature were improved. Details of experimental results are presented in the article.

Anahtar Kelimeler

Deep feature extraction, mango leaf disease, transfer learning, deep learning

Kaynakça

• [1] V. S. L. A. K. C. C. P. K. N. U Sanath Rao, R Swathi, ‘‘Deep learning precision farming: Grapes and mango leaf disease detection by transfer learning,’’ Global Transitions Proceedings, vol. 2, no. 2, pp. 535–544, 2021, international Conference on Computing System and its Applications (ICCSA- 2021).
• [2] R. P. Sampada Gulavnai, ‘‘Deep learning for image based mango leaf disease detection,’’ International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 3S3, pp. 54–56, 2019. [Online]. Available: https://www.ijrte.org/wp-content/uploads/papers/v8i3s3/C10301183S319.pdf
• [3] S. R. Md. Rasel Mia and M. A. Rahman, ‘‘Mango leaf disease recognition using neural network and support vector machine,’’ Iran Journal of Computer Science, vol. 3, pp. 185–193, 2020.
• [4] L. Xu, B. Cao, F. Zhao, S. Ning, P. Xu, W. Zhang, and X. Hou, ‘‘Wheat leaf disease identification based on deep learning algorithms,’’ Physiological and Molecular Plant Pathology, vol. 123, p. 101940, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0885576522001552
• [5] K. G. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, ‘‘Machine learning in agriculture: A review,’’ Sensors, vol. 18, no. 8, 2018. [Online]. Available: https://www.mdpi.com/1424-8220/18/8/2674
• [6] N. Manoharan, V. J. Thomas, and D. Anto Sahaya Dhas, ‘‘Identification of mango leaf disease using deep learning,’’ in 2021 Asian Conference on Innovation in Technology (ASIANCON), 2021, pp. 1–8.
• [7] R. Saleem, J. H. Shah, M. Sharif, M. Yasmin, H.-S. Yong, and J. Cha, ‘‘Mango leaf disease recognition and classification using novel segmentation and vein pattern technique,’’ Applied Sciences, vol. 11, no. 24, 2021. [Online]. Available: https://www.mdpi.com/2076-3417/11/24/11901
• [8] M. Merchant, V. Paradkar, M. Khanna, and S. Gokhale, ‘‘Mango leaf deficiency detection using digital image processing and machine learning,’’ in 2018 3rd International Conference for Convergence in Technology (I2CT), 2018, pp. 1–3.
• [9] Venkatesh, N. Y, S. T. S, S. S, and S. U. Hegde, ‘‘Transfer learning based convolutional neural network model for classification of mango leaves infected by anthracnose,’’ in 2020 IEEE International Conference for Innovation in Technology (INOCON), 2020, pp. 1–7.
• [10] P.Kumar, S. Ashtekar, S. S. Jayakrishna, K. P. Bharath, P. T.Vanathi, and M. RajeshKumar, ‘‘Classification of mango leaves infected by fungal disease anthracnose using deep learning,’’ in 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 2021, pp. 1723–1729.
• [11] U. P. Singh, S. S. Chouhan, S. Jain, and S. Jain, ‘‘Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease,’’ IEEE Access, vol. 7, pp. 43 721–43 729, 2019.
• [12] S. Arya and R. Singh, ‘‘A comparative study of cnn and alexnet for detection of disease in potato and mango leaf,’’ in 2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), vol. 1, 2019, pp. 1–6.
• [13] T. N. Pham, L. V. Tran, and S. V. T. Dao, ‘‘Early disease classification of mango leaves using feed-forward neural network and hybrid metaheuristic feature selection,’’ IEEE Access, vol. 8, pp. 189 960–189 973, 2020.
• [14] W. Tang, Q. Yang, K. Xiong, and W. Yan, ‘‘Deep learning based automatic defect identification of photovoltaic module using electroluminescence images,’’ Solar Energy, vol. 201, pp. 453–460, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0038092X20302875
• [15] S. A. Mohona, S. Aktar, and M. M. Ahamad, ‘‘Efficient computation of leaf disease classification techniques using deep learning,’’ in 2021 3rd International Conference on Electrical Electronic Engineering (ICEEE). IEEE, 2021, pp. 149–152.
• [16] G. S. Tumang, ‘‘Pests and diseases identification in mango using matlab,’’ in 2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST), 2019, pp. 1–4.
• [17] M. M. R. A. P. S. M. T. S. N. R. C. Aditya Rajbongshi, Thaharim Khan, ‘‘Recognition of mango leaf disease using convolutional neural network models: a transfer learning approach,’’ Indonesian Journal of Electrical Engineering and Computer Science, vol. 23, no. 3, pp. 1681–1688, 2021.
• [18] S. I. Ahmed, M. Ibrahim, M. Nadim, M. M. Rahman, M. M. Shejunti, T. Jabid, and M. S. Ali, ‘‘Mangoleafbd: A comprehensive image dataset to classify diseased and healthy mango leaves,’’ 2022. [Online]. Available: https://arxiv.org/abs/2209.02377
• [19] S. I. A. M. N. M. R. M. M. M. S. T. J. Sawkat Ali, Muhammad Ibrahim, ‘‘Mangoleafbd dataset,’’ Online, 2022, 20.10.2024. [Online]. Available: https://data.mendeley.com/datasets/hxsnvwty3r/1
• [20] L. F. Arauz, ‘‘Mango anthracnose: economic impact and current options for integrated management,’’ Plant Disease, vol. 84, pp. 600–611, 2007.
• [21] A. Krizhevsky, I. Sutskever, and G. E. Hinton, ‘‘Imagenet classification with deep convolutional neural networks,’’ Commun. ACM, vol. 60, no. 6, p. 84–90, May 2017. [Online]. Available: https://doi.org/10.1145/3065386
• [22] U. R. Nadeem Akhtar, ‘‘Interpretation of intelligence in cnn-pooling processes: a methodological survey,’’ Neural Computing and Applications, vol. 32, no. 3, pp. 879–898, 2020.
• [23] M. M.-S. J. M.-B. A. S. L. E. E.S. Olivas, J.D.M. Guerrero, ‘‘Transfer learning,’’ in Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, baskı sayısı (opsiyonel) ed., ser. Seri Adı (Opsiyonel), J. S. Lisa Torrey, Ed. Yayıncı Şehri: IGI Global, 2010, vol. Cilt Numarası (Opsiyonel), ch. Bölüm Numarası, pp. 242 – 264.
• [24] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, ‘‘Mobilenets: Efficient convolutional neural networks for mobile vision applications,’’ 2017. [Online]. Available: https://arxiv.org/abs/1704.04861
• [25] F. Chollet, ‘‘Xception: Deep learning with depthwise separable convolutions,’’ 2017. [Online]. Available: https://arxiv.org/abs/1610.02357
• [26] Y. Tai, J. Yang, and X. Liu, ‘‘Image super-resolution via deep recursive residual network,’’ in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2790–2798.
• [27] G. Huang, Z. Liu, and K. Q. Weinberger, ‘‘Densely connected convolutional networks,’’ CoRR, vol. abs/1608.06993, 2016.
• [Online]. Available: http://arxiv.org/abs/1608.06993
• [28] A. Z. Karen Simonyan, ‘‘Very deep convolutional networks for large-scale image recognition,’’ Computer Vision and Pattern Recognition, vol. abs/1608.06993, 2014. [Online]. Available: https://arxiv.org/abs/1409.1556
• [29] F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han, W. J. Dally, and K. Keutzer, ‘‘Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <1mb model size,’’ CoRR, vol. abs/1602.07360, 2016. [Online]. Available: http://arxiv.org/abs/1602.07360
• [30] O. M. Lior Rokach, ‘‘Data mining with decision trees,’’ in Series in Machine Perception and Artificial Intelligence, P. C.-L. Liu, Ed. World Scientific, 2007, pp. 71–86.
• [31] A. I.-A. E. H. Alaa Tharwat, Tarek Gaber, ‘‘Linear discriminant analysis: : A detailed tutorial,’’ AI Communications, vol. 30, no. 2, pp. 169–190, 2017.
• [32] C. C. Aggarwal, Data Classification, 1st ed. Chapman and Hall/CRC, 2014.
• [33] V. V. Corinna Cortes, ‘‘Support-vector networks,’’ Machine Learning, vol. 20, pp. 273–297, 1995.
• [34] M. N. A. H. Sha’abani, N. Fuad, N. Jamal, and M. F. Ismail.
• [35] W. C.-Y. S. Xibin Dong, Zhiwen Yu and Q. Ma, ‘‘A survey on ensemble learning,’’ Frontiers of Computer Science, vol. 14, pp. 241–258, 2020. ECJSE