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@article{200497,
        author = {Dr. Meesala Sudhir Kumar and M. Vamsi Krishna and Fansa Khan and S. Kavya Sri and P. Srinivasul Reddy},
        title = {CropCureNet : Crop Disease Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {1573-1590},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200497},
        abstract = {Agriculture is the backbone of many economies, and maintaining crop health is essential for ensuring food security and sustainable farming. However, plant diseases pose a major threat to crop production, leading to severe yield loss and economic instability. Early and accurate detection of these diseases is therefore critical. This project introduces an AI- based Crop Disease Detection System that uses image processing and deep learning techniques to identify and classify plant diseases efficiently.
The system is trained on a dataset containing images of both healthy and diseased leaves from various crops. Each image in the dataset is preprocessed, normalized, and labeled according to its disease type. When a new or real-time image is provided as input, the system compares it with the trained dataset using a Convolutional Neural Network (CNN) model. The model extracts key visual features such as color variations, texture patterns, and shape distortions to accurately detect the presence and type of disease.
The system aims to support multiple crops and a wide range of diseases, with the ability to expand the dataset for improving the robustness of predictions. Additionally, the model is optimized to run efficiently on mobile devices, ensuring low latency during real-time scanning. By combining cutting-edge deep learning techniques with mobile technology, this project contributes to the larger goal of smart farming and digital agriculture. The expected outcomes include improved disease management, reduced crop loss, increased farmer awareness, and enhanced agricultural sustainability. Ultimately, this deep learning- powered mobile solution offers a scalable and cost-effective tool for empowering farmers and strengthening the agricultural sector.},
        keywords = {Crop Disease Detection, Artificial Intelligence (AI), Deep Learning, Convolutional Neural Network (CNN), Image Processing, Dataset Comparison, Plant Leaf Classification, Feature Extraction, Smart Agriculture, Real-Time Detection, Machine Learning, Precision Farming.},
        month = {May},
        }