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@article{178638,
        author = {Mr. M. Gnanesh Goud and A.Kiran Kumar Reddy and C.Vamshi Karthik and C.Venkatesh},
        title = {IDENTIFICATION OF PLANT DISEASE FROM LEAF IMAGES BASED ON CONVOLUTIONAL NEURAL NETWORKS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4164-4167},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178638},
        abstract = {Plant diseases pose a significant threat to global agriculture, resulting in reduced crop yields and compromised quality. Traditional disease identification methods rely heavily on manual inspection by farmers or agricultural experts, which can be time-consuming, subjective, and prone to errors. In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown remarkable success in image-based disease diagnosis. However, limited datasets in plant phonemics restrict the performance and generalizability of CNN models. This project addresses these challenges by utilizing pre-trained ResNet and DenseNet architectures with transfer learning to enhance plant disease identification accuracy. By leveraging advanced feature extraction capabilities of these models, the system accurately detects and classifies diseases across multiple crop types, even under varying environmental conditions.},
        keywords = {Plant Disease Detection, CNN, Deep Learning, ResNet, DenseNet, Transfer Learning, Precision Agriculture, Image Classification, Sustainable Farming, Crop Protection.},
        month = {May},
        }