CNN-Based Identification of Rice Leaf Diseases
DOI:
https://doi.org/10.32628/IJSRST25123120Keywords:
Convolutional Neural Network (CNN), Rice Leaf Disease, Image Classification, Precision AgricultureAbstract
The identification of rice leaf diseases through visual inspection remains a challenge due to the need for expert knowledge and the high similarity among various disease symptoms. This study aims to develop an automatic classification system using a Convolutional Neural Network (CNN) to identify rice leaf diseases based on leaf images. The dataset comprises 15,030 images distributed across nine classes: eight diseases and one healthy category. The data were split into training, validation, and testing subsets with an 80:10:10 ratio. The CNN model was built using TensorFlow and Keras on the Google Colab platform and trained for 13 epochs with image normalization and data augmentation techniques to improve generalization. The model achieved a training accuracy of 89.30%, a validation accuracy of 85.64%, and a test accuracy of 84.52%. These results demonstrate that the CNN model can effectively classify rice leaf images with high precision, highlighting its potential application in intelligent agricultural diagnostics.
Downloads
References
K. Adi, C. E. Widodo, A. P. Widodo, A. Setiadi, and A. Setiawan, “Identification of Rice Plant Diseases Using Convolutional Neural Network Method,” Mathematical Modelling of Engineering Problems, vol. 11, no. 5, pp. 1279–1285, 2024, doi: 10.18280/mmep.110517.
S. Skendžić, M. Zovko, I. P. Živković, V. Lešić, and D. Lemić, “The impact of climate change on agricultural insect pests,” May 01, 2021, MDPI AG. doi: 10.3390/insects12050440.
J. Pretty and Z. P. Bharucha, “Integrated pest management for sustainable intensification of agriculture in Asia and Africa,” Insects, vol. 6, no. 1, pp. 152–182, Mar. 2015, doi: 10.3390/insects6010152.
S. K. Dara, “The New Integrated Pest Management Paradigm for the Modern Age,” J Integr Pest Manag, vol. 10, no. 1, Jan. 2019, doi: 10.1093/jipm/pmz010.
M. H. Masood, H. Saim, M. Taj, and M. M. Awais, “Early Disease Diagnosis for Rice Crop,” Apr. 2020, [Online]. Available: http://arxiv.org/abs/2004.04775
R. Li et al., “Predicting rice diseases using advanced technologies at different scales: present status and future perspectives,” Dec. 01, 2023, Springer. doi: 10.1007/s42994-023-00126-4.
K. S. Archana and A. Sahayadhas, “Automatic rice leaf disease segmentation using image processing techniques,” International Journal of Engineering and Technology (UAE), vol. 7, no. 3.27 Special Issue 27, pp. 182–185, 2018, doi: 10.14419/ijet.v7i3.27.17756.
M. N. Abu Bakar, A. H. Abdullah, N. A. Rahim, H. Yazid, S. N. Misman, and M. J. Masnan, “Rice Leaf Blast Disease Detection Using Multi-Level Colour Image Thresholding”.
S. Ramesh and D. Vydeki, “Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm,” Information Processing in Agriculture, vol. 7, no. 2, pp. 249–260, Jun. 2020, doi: 10.1016/j.inpa.2019.09.002.
M. Shoaib, A. Sadeghi-Niaraki, F. Ali, I. Hussain, and S. Khalid, “Leveraging deep learning for plant disease and pest detection: a comprehensive review and future directions,” 2025, Frontiers Media SA. doi: 10.3389/fpls.2025.1538163.
M. Trigka and E. Dritsas, “A Comprehensive Survey of Deep Learning Approaches in Image Processing,” Sensors, vol. 25, no. 2, Jan. 2025, doi: 10.3390/s25020531.
L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” J Big Data, vol. 8, no. 1, Dec. 2021, doi: 10.1186/s40537-021-00444-8.
R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks: an overview and application in radiology,” Aug. 01, 2018, Springer Verlag. doi: 10.1007/s13244-018-0639-9.
M. A. Azim, M. K. Islam, M. M. Rahman, and F. Jahan, “An effective feature extraction method for rice leaf disease classification,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 19, no. 2, pp. 463–470, Apr. 2021, doi: 10.12928/TELKOMNIKA.v19i2.16488.
A. Kamilaris and F. X. Prenafeta-Boldú, “A review of convolutional neural networks in agriculture,” Apr. 01, 2018, Cambridge University Press. doi: 10.1017/S0021859618000436.
J. Lu, L. Tan, and H. Jiang, “Review on convolutional neural network (CNN) applied to plant leaf disease classification,” Aug. 01, 2021, MDPI AG. doi: 10.3390/agriculture11080707.
H. Ning, S. Liu, Q. Zhu, and T. Zhou, “Convolutional neural network in rice disease recognition: accuracy, speed and lightweight,” 2023, Frontiers Media SA. doi: 10.3389/fpls.2023.1269371.
P. M. Paithane and S. N. Kakarwal, “International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING Automatic Pancreas Segmentation using A Novel Modified Semantic Deep Learning Bottom-Up Approach,” Original Research Paper International Journal of Intelligent Systems and Applications in Engineering IJISAE, vol. 2022, no. 1, pp. 98–104, doi: 10.1039/b000000x.
A. Sony, “Prediction of Rice Diseases Using Convolutional Neural Network (in Rstudio),” 2019. [Online]. Available: www.ijisrt.com595
M. Lakshmi, M. Swethasupriya, J. Nandini, and S. Sai, “CNN-based Image Identification with Python,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 9, 2020, doi: 10.17148/IJARCCE.2020.9312.
V. Shankar and M. R. Femila Goldy, “LEAF DISEASE DETECTION USING PYTHON,” International Journal of Advanced Research in Computer and Communication Engineering Impact Factor, vol. 8, no. 9, 2023, doi: 10.17148/IJARCCE.2023.12902.
A. Rathod, D. Panchal, N. Minocha, and D. Bhatt, “Crop Disease Detection Using Lightweight Deep Learning Model for Smartphone.”
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
