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@article{195664,
        author = {Kamesh S and Madan Kumaar K M and Kumaresan P and Manikandan K and Ganesh K R},
        title = {SMART CROP DISEASE DETECTION USING ARTIFICIAL INTELLIGENCE},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {837-840},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195664},
        abstract = {This project, titled Smart Crop Disease Detection Using Artificial Intelligence, focuses on developing an intelligent system to identify plant diseases using deep learning techniques. Early detection of crop diseases is essential to reduce agricultural losses and ensure food security. The system uses a dataset of crop leaf images, which are preprocessed using techniques such as resizing and normalization to improve image quality. Advanced deep learning models like EfficientNetV2B2 and ResNet50 are trained to classify diseases based on visual symptoms such as spots, discoloration, and texture changes. The models are evaluated using performance metrics such as accuracy and loss, achieving high prediction accuracy. The system determines the disease by selecting the class with the highest probability output. This approach reduces the need for manual inspection and enables faster and more reliable diagnosis. Overall, the proposed system helps farmers take timely action, improve crop yield, and supports sustainable and precision agriculture practices.},
        keywords = {Artificial Intelligence, Deep Learning, Plant Disease Detection, Image Processing, Convolutional Neural Network (CNN), Precision Agriculture, Crop Health Monitoring, Machine Learning},
        month = {April},
        }