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@article{173973,
        author = {Ms. VINZUDA NIDHI KAUSHIKBHAI and Ms. Nirali P. Borad and Ms. Dolly R. Raja},
        title = {Rice Leaf Disease Detection using Machine Learning: A Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2189-2191},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173973},
        abstract = {Rice is one of the most essential food crops worldwide, and its productivity is significantly affected by various diseases. Early detection and classification of rice leaf diseases can help prevent large-scale yield loss. This review paper provides a comparative study of various machine learning (ML) and deep learning (DL) approaches used for rice leaf disease detection. We analyze key methodologies, datasets, and performance metrics across multiple research studies. The findings indicate that deep learning models, particularly CNN-based architectures, achieve superior accuracy compared to traditional ML techniques. Furthermore, IoT and edge intelligence have emerged as promising trends for real-time disease monitoring in precision agriculture.},
        keywords = {Image Processing, Machine Learning, Random Forest, Rice Leaf Disease, Support Vector Machine (SVM)},
        month = {March},
        }