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@article{173523,
        author = {Lakshmi Saraswathi P V and Harini J and Hari Priya S and Boobalan M},
        title = {Pest Detection and Classification using Convolutional Neural Network},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {537-542},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173523},
        abstract = {The proposed system utilizes a large dataset of pest images, encompassing multiple species and varying environmental conditions, to train a deep learning model capable of high-precision classification. The dataset undergoes extensive preprocessing, including image augmentation, normalization, and noise reduction, to enhance model performance. The CNN model extracts spatial hierarchies of features, enabling it to distinguish between different pest species effectively. The trained model is deployed in an interactive web application, allowing farmers to upload images of pests for real-time classification. Additionally, the system provides pest control recommendations based on the identified species, assisting in decision-making for pesticide application and biological control methods. This approach enhances agricultural productivity by enabling timely pest management, reducing pesticide overuse, and minimizing crop losses.},
        keywords = {},
        month = {March},
        }