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@article{162520,
        author = {Mididoddi Keerthi and Donda Honey and Anumula Bhuvana and R.Kanchana},
        title = {Automated Leaf Disease Detection:A CNN Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {10},
        pages = {296-302},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162520},
        abstract = {Leaf diseases pose significant threats to agricultural productivity and food security worldwide. Traditional methods of disease detection often rely on manual inspection, which can be time-consuming and subjective. In this study, a novel approach for automated leaf disease detection using Convolutional Neural Networks (CNNs) is proposed. The method involves training a CNN model on a dataset of labeled leaf images to learn distinctive features associated with different types of diseases. Transfer learning techniques are utilized to leverage pre-trained CNN architectures, enhancing the model's performance with limited data. Experimental results demonstrate the efficacy of the approach in accurately identifying various leaf diseases, achieving high levels of accuracy, precision, and recall. The proposed method offers a promising solution for early disease detection, enabling timely interventions to mitigate crop losses and enhance agricultural sustainability. },
        keywords = {image processing, feature extraction, classification, detection, CNN },
        month = {},
        }