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@article{181475,
        author = {Bhoir Namita Vinod and Dr. Adokar D. U. and Korde S. G. and Gaikwad K.B.},
        title = {DEEP NEURAL NETWORKS FOR OBJECT DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {3863-3867},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181475},
        abstract = {With increase in population the need for food is on rise, in such circumstances, plant diseases prove to be a major threat to agricultural produce and result in disastrous consequences for farmers. Early detection of plant disease can help in ensuring food security and controlling financial losses. The images of diseased plants can be used to identify the diseases. Classification abilities of Convolution Neural Networks are used to obtain reliable output. Google’s pre trained model ‘Inception v3’ is used. The Inception v3 model is trained over a dataset of diseased plants obtained from ‘Plant Village Dataset’. The developed detection approach is evaluated on measures of F1 score, precision and recall.},
        keywords = {Plant disease, image processing, image acquisition, segmentation, feature extraction, classification.},
        month = {June},
        }