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@article{169601,
        author = {YASASWI GALI and G. MANEESHA and SAI DEEPIKA and SUMIYA ANJUM BEGAM and VINOJ J},
        title = {Leaf Disease Detection By Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {1989-1994},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169601},
        abstract = {This paper presents a deep learning approach for leaf disease detection using Convolution Arithmetic, Transfer Learning, and Batch Gradient Descent. Convolution Arithmetic within CNNs extracts key features from leaf images, identifying disease patterns like texture and color changes. Transfer Learning applies pre-trained models, reducing training time and improving accuracy. Batch Gradient Descent ensures efficient optimization for faster convergence. These techniques combined create an effective framework for accurate and efficient leaf disease detection.},
        keywords = {Leaf Disease Detection, Digital Image Processing, Data Collection and Preprocessing, Image Segmentation, Feature Extraction},
        month = {November},
        }