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@article{204018,
        author = {Abhiram Muraleedharan Nair and Dhiraj Jadhav and Jasleen Kaur Multani and Girish Nalkar and Divyanshi Nagrale},
        title = {Crop Disease Detection using CNN and Yield Prediction Model with Web Integration},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {1171-1177},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204018},
        abstract = {In recent years, the combination of artificial intelligence (AI) and computer vision has continued to provide good results in dealing with the problems of agriculture. The authors of the research pointed out that one of the future areas of crop management, early disease detection, has been one of the main problems. A deep learning-based method powered by Convolutional Neural Networks (CNN) has been suggested with MobileNetV2 architecture as a means to successfully diagnose plant diseases. The mobile camera that captures the plants at periodic intervals is the hardware component of the solution that is subject to the CNN model trained on publicly available datasets of crop diseases. The application is hosted in the form of a web-based system whose frontend is developed in Bootstrap while backend integration is done using FastAPI/Flask, MySQL, and Weather APIs. The system is primarily designed for disease detection but it also has a machine learning-based yield prediction module in its architecture that estimates the crop yield based on agricultural inputs and prevailing environmental factors such as humidity, temperature, and precipitation. The results indicate that the system is capable of attaining high accuracy while keeping the computational cost low, thus making it useful for both real-time and edge-based applications. Farmers are provided with instant feedback through a user-friendly interface of the solution which makes it possible to control outbreaks of diseases in advance and promotes organic farming methods.},
        keywords = {Precision Agriculture, Crop Disease Detection, Yield Prediction, Deep Learning, Machine Learning, Computer Vision},
        month = {June},
        }