Citation :

A. Dhanalakshmi, K. Balasubramanian, "AI-Driven Rice Leaf Disease Detection Leveraging Texture-Feature Fusion and Low-Dimensional Feature Spaces," International Journal of Electronics and Communication Engineering, vol. 13, no. 5, pp. 171-180, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I5P115

Abstract

Rice leaf diseases pose a big threat to crop production in the world, and hence the importance of early and proper identification of the diseases to ensure a significant contribution in Fusion. The manual inspection is lengthy, biased, and not very uniform in the large farmlands. The paper introduces an AI-based framework of rice leaf disease determination, combining texture-feature Fusion with low-dimensional feature space maximization to improve the level of classification at the feature dimension. Multi-scale texture patterns were extracted by the system based on Gray-Level Co-occurrence Matrices (GLCM), Local Binary Patterns (LBP), and Gabor; they are combined with color-gradient descriptors to compose a hybrid feature single feature vector. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used to reduce dimensions to a smaller and more discriminative space of features that can be used in lightweight models. Evaluated on standard rice leaf disease data, it has been demonstrated that the fused features are more accurate than training and validation, differences in illumination, and damage to some parts of the leaf. The findings show significant enhancements in the detection efficacy, especially of the early symptoms. In real life, practical constraints are that it requires varying and region-specific data, extreme light sensitivity when used in real fields, and the problem of a confusion matrix for rice leaf disease detection. The accuracy comparison and performance curve efforts should aim to deploy edges in real-time, add hyperspectral cues, and domain-generalization adaptively to make the model effective in various ecological regimes and rice varieties.

Keywords

Crop Monitoring, Rice Leaf Disease Detection, Texture Feature Fusion, Low-Dimensional Feature Spaces, PCA, LDA, Computer Vision In Agriculture, AI-Based.

References

1. Abhishek Upadhyay et al., Leveraging AI and ML in Precision Farming for Pest and Disease Management: Benefits, Challenges, and Future Prospects, Ecologically Mediated Development, pp. 511-528, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
2. Munir Majdalawieh et al., “Precision Agriculture in the Age of AI: A Systematic Review of Machine Learning Methods for Crop Disease Detection,” Smart Agricultural Technology, vol. 12, pp. 1-12, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
3. Agnijit Basu, and Abhay Narayan, “The Role of Machine Learning in Transforming Agricultural Practices: Insights Into Crop Yield Optimization and Disease Detection,” Iran Journal of Computer Science, vol. 8, pp. 1199-1217, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
4. Juan Ma, Zeqiang Cheng, and Yanyong Cao, “Artificial Intelligence-Assisted Breeding for Plant Disease Resistance,” International Journal of Molecular Sciences, vol. 26, no. 11, pp. 1-17, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
5. Amna Syeda, “Harnessing Multi-Omics and Genome-Editing Technologies for Climate-Resilient Agriculture: Bridging AI-driven Insights with Sustainable Crop Improvement,” Plant Molecular Biology, vol. 115, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
6. Doni Thingujam et al., “Climate-Resilient Crops: Integrating AI, Multi-Omics, and Advanced Phenotyping to Address Global Agricultural and Societal Challenges,” Plants, vol. 14, no. 17, pp. 1-40, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
7. S. Ajith, S. Vijayakumar, and N. Elakkiya, “Yield Prediction, Pest and Disease Diagnosis, Soil Fertility Mapping, Precision Irrigation Scheduling, and Food Quality Assessment using Machine Learning and Deep Learning Algorithms,” Discover Food, vol. 5, pp. 1-23, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
8. Karishma Kumar et al., “AI-Driven Future Farming: Achieving Climate-Smart and Sustainable Agriculture,” AgriEngineering, vol. 7, no. 3, pp. 1-30, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
9. Chijioke Leonard Nkwocha, and Abhilash Kumar Chandel, “Towards an End-to-End Digital Framework for Precision Crop Disease Diagnosis and Management Based on Emerging Sensing and Computing Technologies: State over Past Decade and Prospects,” Computers, vol. 14, no. 10, pp. 1-69, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
10. Zulfiqar Ali et al., “Artificial Intelligence for Sustainable Agriculture: A Comprehensive Review of AI-Driven Technologies in Crop Production,” Sustainability, vol. 17, no. 5, pp. 1-24, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
11. My Abdelmajid Kassem, “Harnessing Artificial Intelligence and Machine Learning for Identifying Quantitative Trait Loci (QTL) Associated with Seed Quality Traits in Crops,” Plants, vol. 14, no. 11, pp. 1-17, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
12. Marieme Seif-Ennasr et al., “Harvesting Insights on AI-Driven Technologies in Modern Agriculture: A Systematic Literature Review,” IEEE Access, vol. 13, pp. 143713-143736, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
13. Mansoor Alghamdi, and Yasmeen G. Haraz, “Smart Biofloc Systems: Leveraging Artificial Intelligence (AI) and Internet of Things (IoT) for Sustainable Aquaculture Practices,” Processes, vol. 13, no. 7, pp. 1-26, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
14. J. Siva Prashanth et al., “Smart Farming Revolution: A Cutting-Edge Review of Deep Learning and IoT Innovations in Agriculture,” Operations Research Forum, vol. 6, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
15. Benjamin Ilo et al., “Artificial Intelligence in Rice Quality and Milling: Technologies, Applications, and Future Prospects,” Processes, vol. 13, no. 11, pp. 1-34, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
16. Burak Gülmez et al., “A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture,” Potato Research, vol. 68, no. 2, pp. 1295-1329, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
17. Garima Gupta, and Sudhir Kumar Pal, “Applications of AI in Precision Agriculture,” Discover Agriculture, vol. 3, pp. 1-19, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
18. Blossom Kaler, and Amandeep Kaur, “A Systematic Survey on Explainable Artificial Intelligence (XAI) for Plant Health Monitoring: Challenges and Opportunities,” Applied Intelligence, vol. 55, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
19. Evgenia Gkintoni et al., “Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review,” Healthcare, vol. 13, no. 15, pp. 1-81, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
20. Donald D. Haines et al., “Leveraging Artificial Intelligence and Modulation of Oxidative Stressors to Enhance Healthspan and Radical Longevity,” Biomolecules, vol. 15, no. 11, pp. 1-28, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]