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@article{193064,
        author = {NEERAJ KUMAR and DR. AJAY SINGH},
        title = {A Systematic and Analytical Review of Machine Learning and Deep Learning Techniques for Plant Leaf Disease Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {4013-4020},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193064},
        abstract = {Plant leaf diseases significantly affect agricultural productivity and global food security. Traditional disease detection methods depend on manual inspection, which is time-consuming and subjective. Recent advancements in machine learning (ML) and deep learning (DL) have enabled automated, image-based plant disease detection systems. This paper presents a systematic and analytical review of ML and DL techniques used for plant leaf disease detection. Existing studies are categorized based on feature extraction strategies, classification models, datasets, and evaluation metrics. The review identifies major research gaps, including limited real-field validation, dataset imbalance, computational complexity, and lack of explainability. The study emphasizes the need for hybrid and explainable AI-based approaches for reliable agricultural deployment.},
        keywords = {Plant leaf disease detection, Machine learning, Deep learning, Explainable AI, Smart agriculture},
        month = {February},
        }