2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 14 , Issue 2 , PP: 01-07, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Eman Turki Mahdi 1 * , Wijdan Jaber AL-kubaisy 2 , Maha Mahmood 3
Doi: https://doi.org/10.54216/JISIoT.140201
Deep Learning is a high-performance machine learning approach that combines supervised machine learning and feature learning. It is built of a sophisticated models with numerous hidden layers and neurons to create advanced image processing models. DL has proven its effectiveness and resilient in different fields including big data, computer vision, image processing, and many others. In agriculture, rice leaf infections are a frequent and pervasive issue that lower crop and output. This research proposed a reduced form of Capsule Network (Caps NET), a form convolutional neural network, for the classification of rice leaf disease. The goal of the suggested Caps NET model was to assess the suitability of various feature learning models and enhance deep learning models' capacity to learn about rice leaf disease classification. Caps NET was fed images of both healthy and infected leaves. High classification performance was obtained with the ideal configuration (FC1 (960), FC2 (768), and FC3 (4096)), which had 96.66% accuracy, 97.25% sensitivity, and 97.49% specificity.
Capsule Network , Deep Learning , Convolutional neural networks , Rice leaf disease classification
[1] M. W. Rosegrant and S. A. Cline, “Global Food Security: Challenges and Policies,” Science, 2003, doi: 10.1126/science.1092958.
[2] Khalid, M. M., & Karan, O. (2023). Deep Learning for Plant Disease Detection. International Journal of Mathematics, Statistics, and Computer Science, 2, 75–84. https://doi.org/10.59543/ijmscs.v2i.8343
[3] Mohammed, M.A., Lakhan, A., Abdulkareem, K.H., Almujally, N.A., Bourair, A.A., Memon, S., Marhoon, H.A. and Martinek, R., 2024. Edge-Cloud Remote Sensing Data Based Plant Disease Detection Using Deep Neural Networks With Transfer Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. vol. 17, pp. 11219-11229, 2024, doi: 10.1109/JSTARS.2024.3410515.
[4] S. Phadikar, “Classification of Rice Leaf Diseases Based on Morphological Changes,” Int. J. Inf. Electron. Eng., 2012, doi: 10.7763/ijiee.2012.v2.137.
[5] T. Islam, M. Sah, S. Baral, and R. Roychoudhury, “A Faster Technique on Rice Disease Detection using Image Processing of Affected Area in Agro-Field,” in Proceedings of the International Conference on Inventive Communication and Computational Technologies, ICICCT 2018, 2018, doi: 10.1109/ICICCT.2018.8473322.
[6] C. U. Kumari, S. Jeevan Prasad, and G. Mounika, “Leaf Disease Detection: Feature Extraction with K-means clustering and Classification with ANN,” in 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), 2019, pp. 1095–1098, doi: 10.1109/ICCMC.2019.8819750.
[7] Mahdi, E.T., Awad, W.K., Rasheed, M.M., & Mahdi, A.T. (2023). Proposed Security System for Cities Based on Animal Recognition Using IoT and Clouds. In Proceedings - International Conference on Developments in eSystems Engineering (DeSE) (pp. 834–839).
[8] S. S. Chouhan, A. Kaul, U. P. Singh, and S. Jain, “Bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases: Anautomatic approach towards plant pathology,” IEEE Access, 2018, doi: 10.1109/ACCESS.2018.2800685.
[9] S. Kumar, B. Sharma, V. K. Sharma, H. Sharma, and J. C. Bansal, “Plant leaf disease identification using exponential spider monkey optimization,” Sustain. Comput. Informatics Syst., 2018, doi: 10.1016/j.suscom.2018.10.004.
[10] S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification,” Comput. Intell. Neurosci., 2016, doi: 10.1155/2016/3289801.
[11] S. H. Lee, C. S. Chan, S. J. Mayo, and P. Remagnino, “How deep learning extracts and learns leaf features for plant classification,” Pattern Recognit., 2017, doi: 10.1016/j.patcog.2017.05.015.
[12] J. Amara, B. Bouaziz, and A. Algergawy, “A deep learning based approach for banana leaf diseases classification,” in Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI), 2017.
[13] D. C. Cireşan, U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber, “Flexible, high performance convolutional neural networks for image classification,” in IJCAI International Joint Conference on Artificial Intelligence, 2011, doi: 10.5591/978-1-57735-516-8/IJCAI11-210.
[14] G. Altan, Y. Kutlu, and A. Gökçen, “Chronic obstructive pulmonary disease severity analysis using deep learning on multi-channel lung sounds,” TURKISH J. Electr. Eng. Comput. Sci., 2020, doi: 10.3906/elk-2004-68.
[15] S. Arivazhagan, R. N. Shebiah, S. Ananthi, and S. Vishnu Varthini, “Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features,” Agric. Eng. Int. CIGR J., 2013.
[16] M. Kwabena Patrick, A. Felix Adekoya, A. Abra Mighty, and B. Y. Edward, “Capsule network ule Networks – A survey,” Journal of King Saud University - Computer and Information Sciences. 2019, doi: 10.1016/j.jksuci.2019.09.014.
[17] K. Zhang, Z. Xu, S. Dong, C. Cen, and Q. Wu, “Identification of peach leaf disease infected by Xanthomonas campestris with deep learning,” Eng. Agric. Environ. Food, 2019, doi: 10.1016/j.eaef.2019.05.001.
[18] P. K., Barpanda, N. K., Rath, A. K., & Behera, S. K. (2020). "Deep feature based rice leaf disease identification using support vector machine," published in Computers and Electronics in Agriculture, Volume 175, page 105527. DOI: 10.1016/j.compag.2020.105527.
[19] G. Geetharamani and A. P. J., “Identification of plant leaf diseases using a nine-layer deep convolutional neural network,” Comput. Electr. Eng., 2019, doi: 10.1016/j.compeleceng.2019.04.011.
[20] S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Front. Plant Sci., 2016, doi: 10.3389/fpls.2016.01419.
[21] S. Sabour, N. Frosst, and G. E. Hinton, “Dynamic routing between Capsule network ,” in Advances in Neural Information Processing Systems, 2017.
[22] S. Verma, A. Chug, and A. P. Singh, “Exploring Capsule network ule networks for disease classification in plants,” J. Stat. Manag. Syst., 2020, doi: 10.1080/09720510.2020.1724628.
[23] M. Dong, S. Mu, T. Su, and W. Sun, “Image Recognition of Peanut Leaf Diseases Based on Capsule network ule Networks,” 2019, pp. 43–52.
[24] Zhang, L., Wang, X., & Liu, Y. (2020). "Rice Leaf Disease Identification Using Convolutional Neural Networks." Agricultural Informatics, 15(4), 200-212.
[25] Singh, D., Singh, V., & Gupta, M. (2021). "Enhanced CNN Model for Rice Leaf Disease Classification." Biosystems Engineering, 200, 35-45.
[26] Chen, X., He, Y., & Zhang, J. (2019). "CNN-Based Approach for Rice Leaf Disease Detection." IEEE Access, 7, 171264-171273.
