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@article{189080,
        author = {Srujan P and Uday Kumar S and Rohan Bhat and Pavan S and Nisha Wilvicta J},
        title = {CropSense: An AI-Powered Agricultural Decision Support System for Optimal Crop Recommendation and Disease Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {4785-4789},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189080},
        abstract = {Agriculture faces critical challenges including cli- mate variability, soil degradation, and the need for sustainable farming practices. This paper presents CropSense, an intelligent agricultural decision support system leveraging machine learning and computer vision for data-driven crop recommendations and disease detection. The system integrates soil nutrient levels (N- P-K), pH values, temperature, humidity, and rainfall data to predict optimal crop selection. Additionally, it incorporates deep learning techniques for plant disease identification across 38 disease categories. Experimental results demonstrate 95.1% ac- curacy in crop recommendation using an ensemble approach and 93.8% accuracy in disease classification using transfer learning with ResNet-50. The Flask-based web application provides an intuitive interface for farmers, promoting sustainable agricultural practices and improved crop yields.},
        keywords = {Precision agriculture, machine learning, crop recommendation, disease detection, transfer learning, ensemble methods, decision support systems},
        month = {December},
        }