Artificial Intelligence-Based Rice Variety Classification : A Comprehensive Review
DOI:
https://doi.org/10.32628/CSEIT261214Keywords:
Rice Variety Classification, Deep Learning, Computer Vision, Smart Agriculture, Artificial IntelligenceAbstract
Rice variety classification plays a crucial role in ensuring food quality, agricultural productivity, seed purity, and market value. Traditional classification methods rely heavily on expert knowledge and manual inspection, which are time-consuming, subjective, and prone to error. With the rapid advancement of artificial intelligence (AI), computer vision, and deep learning techniques, automated rice variety classification has gained significant research attention. Recent studies have explored convolutional neural networks, vision transformers, transfer learning, and ensemble learning models to improve classification accuracy using grain-level, leaf-level, and field-level images. These approaches leverage feature extraction, attention mechanisms, and data augmentation to handle intra-class similarity and inter-class variation among rice varieties. This review provides a systematic overview of state-of-the-art AI-based rice variety classification techniques, highlighting methodologies, datasets, advantages, and limitations reported in recent literature. Furthermore, it analyzes research findings, identifies key challenges such as dataset scarcity, environmental variability, and model generalization, and outlines future research directions. The study aims to serve as a comprehensive reference for researchers and practitioners seeking to develop robust, scalable, and interpretable rice variety classification systems using modern AI techniques.
Downloads
References
M. M. Islam, G. M. S. Himel, M. G. Moazzam, and M. S. Uddin, “Artificial Intelligence-based Rice Variety Classification: A State-of-the-art Review and Future Directions,” Smart Agricultural Technology, vol. 10, no. December 2024, p. 100788, 2025, doi: 10.1016/j.atech.2025.100788. DOI: https://doi.org/10.1016/j.atech.2025.100788
E. T. Baek, “Attention Score-Based Multi-Vision Transformer Technique for Plant Disease Classification,” Sensors, vol. 25, no. 1, 2025, doi: 10.3390/s25010270. DOI: https://doi.org/10.3390/s25010270
N. Hashim et al., “Smart Farming for Sustainable Rice Production: An Insight into Application, Challenge, and Future Prospect,” Rice Science, vol. 31, no. 1, pp. 47–61, 2024, doi: 10.1016/j.rsci.2023.08.004. DOI: https://doi.org/10.1016/j.rsci.2023.08.004
D. S. Joseph, P. M. Pawar, and K. Chakradeo, “Real-Time Plant Disease Dataset Development and Detection of Plant Disease Using Deep Learning,” IEEE Access, vol. 12, no. January, pp. 16310–16333, 2024, doi: 10.1109/ACCESS.2024.3358333. DOI: https://doi.org/10.1109/ACCESS.2024.3358333
M. Z. Uddin, M. N. Mahamood, A. Ray, M. I. Pramanik, F. Alnajjar, and M. A. R. Ahad, “E2ETCA: End-to-end training of CNN and attention ensembles for rice disease diagnosis1,” Journal of Integrative Agriculture, 2024, doi: 10.1016/j.jia.2024.03.075. DOI: https://doi.org/10.1016/j.jia.2024.03.075
K. Mahadevan, A. Punitha, and J. Suresh, “Automatic recognition of Rice Plant leaf diseases detection using deep neural network with improved threshold neural network,” e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 8, no. July 2023, p. 100534, 2024, doi: 10.1016/j.prime.2024.100534. DOI: https://doi.org/10.1016/j.prime.2024.100534
M. H. Bijoy et al., “Towards Sustainable Agriculture: A Novel Approach for Rice Leaf Disease Detection Using dCNN and Enhanced Dataset,” IEEE Access, vol. 12, no. March, pp. 34174–34191, 2024, doi: 10.1109/ACCESS.2024.3371511. DOI: https://doi.org/10.1109/ACCESS.2024.3371511
R. K. Rachman, D. R. I. M. Setiadi, A. Susanto, K. Nugroho, and H. M. M. Islam, “Enhanced Vision Transformer and Transfer Learning Approach to Improve Rice Disease Recognition,” Journal of Computing Theories and Applications, vol. 1, no. 4, pp. 446–460, 2024, doi: 10.62411/jcta.10459. DOI: https://doi.org/10.62411/jcta.10459
N. Sunandar and J. Sutopo, “Utilization of Artificial Neural Network in Rice Plant Disease Classification Using Leaf Image,” International Journal of Research In Science & Engineering, no. 42, pp. 1–10, 2024, doi: 10.55529/ijrise.42.1.10. DOI: https://doi.org/10.55529/ijrise.42.1.10
P. K. Mannepalli, A. Pathre, G. Chhabra, P. A. Ujjainkar, and S. Wanjari, “Diagnosis of bacterial leaf blight, leaf smut, and brown spot in rice leafs using VGG16,” Procedia Computer Science, vol. 235, pp. 193–200, 2024, doi: 10.1016/j.procs.2024.04.022. DOI: https://doi.org/10.1016/j.procs.2024.04.022
R. S. Jesie, M. S. Godwin Premi, and T. Jarin, “Comparative analysis of paddy leaf diseases sensing with a hybrid convolutional neural network model,” Measurement: Sensors, vol. 31, no. August 2023, p. 100966, 2024, doi: 10.1016/j.measen.2023.100966. DOI: https://doi.org/10.1016/j.measen.2023.100966
V. Rajasekhar, G. Arulselvi, and K. S. Babu, “Design an optimization based ensemble machine learning framework for detecting rice leaf diseases,” Multimedia Tools and Applications, no. April, 2024, doi: 10.1007/s11042-024-19134-7. DOI: https://doi.org/10.1007/s11042-024-19134-7
Khalid, Zainab, H. Tauseef, A. Shabbir, N. Iqbal, and A. F. Sahar Zia, “ADeep Learning-Based Technique for Classification of Rice Leaf Disease Using Transfer Learning,” Kurdish Studies, vol. 12, no. 2, pp. 5462–5478, 2024.
J. G. Kotwal, R. Kashyap, and P. M. Shafi, Artificial Driving based EfficientNet for Automatic Plant Leaf Disease Classification, vol. 83, no. 13. 2024. doi: 10.1007/s11042-023-16882-w. DOI: https://doi.org/10.1007/s11042-023-16882-w
A. Chakrabarty, S. T. Ahmed, M. F. U. Islam, S. M. Aziz, and S. S. Maidin, “An interpretable fusion model integrating lightweight CNN and transformer architectures for rice leaf disease identification,” Ecological Informatics, vol. 82, no. March, p. 102718, 2024, doi: 10.1016/j.ecoinf.2024.102718. DOI: https://doi.org/10.1016/j.ecoinf.2024.102718
W. I. A. E. Altabaji, M. Umair, W. H. Tan, Y. L. Foo, and C. P. Ooi, “Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification,” IEEE Access, vol. 12, no. January, pp. 36622–36635, 2024, doi: 10.1109/ACCESS.2024.3373000. DOI: https://doi.org/10.1109/ACCESS.2024.3373000
F. M. Firnando, D. R. I. M. Setiadi, A. R. Muslikh, and S. W. Iriananda, “Analyzing InceptionV3 and InceptionResNetV2 with Data Augmentation for Rice Leaf Disease Classification,” Journal of Future Artificial Intelligence and Technologies, vol. 1, no. 1, pp. 1–11, 2024, doi: 10.62411/faith.2024-4. DOI: https://doi.org/10.62411/faith.2024-4
C. G. Simhadri, H. K. Kondaveeti, V. K. Vatsavayi, A. Mitra, and P. Ananthachari, “Deep learning for rice leaf disease detection: A systematic literature review on emerging trends, methodologies and techniques,” Information Processing in Agriculture, no. April, 2024, doi: 10.1016/j.inpa.2024.04.006. DOI: https://doi.org/10.1016/j.inpa.2024.04.006
P. I. Ritharson, K. Raimond, X. A. Mary, J. E. Robert, and A. J, “DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes,” Artificial Intelligence in Agriculture, vol. 11, pp. 34–49, 2024, doi: 10.1016/j.aiia.2023.11.001. DOI: https://doi.org/10.1016/j.aiia.2023.11.001
X. Bi and H. Wang, “Double-branch deep convolutional neural network-based rice leaf diseases recognition and classification,” Journal of Agricultural Engineering, vol. LV, 2023, doi: 10.4081/jae.2023.1544. DOI: https://doi.org/10.4081/jae.2023.1544
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
