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@article{194399,
        author = {Shalvi Kale and Suhani Tanawade and Rushikesh Shinde and Dattatray Sul and Tejas Somwanshi and Prof. M. A. Gade},
        title = {Crop Disease Prediction and Stage Risk Assessment using YOLO v11},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {4051-4054},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194399},
        abstract = {Agriculture is the cornerstone of the India’s economy, contributing substantially to national GDP and employment. However, crop diseases remain a major obstacle to food security and economic stability. These diseases often go undetected until the later stages, leading to significant yield loss. This research presents an AI-powered system that integrates YOLO v11 for crop disease detection, Long Short-Term Memory (LSTM) for stage-wise risk assessment, and a recommendation engine for treatment guidance. The system identifies disease symptoms through leaf image analysis, classifies severity levels, and suggests optimized treatments based on the predicted stage. The use of deep learning techniques allows for real-time detection and classification, significantly reducing farmers’ dependency on manual diagnosis. The solution’s web and mobile interface ensure ease of accessibility for rural communities. This model promotes precision agriculture by minimizing losses, optimizing pesticide use, and contributing to sustainable farming practices.},
        keywords = {Agriculture, Crop Disease Detection, YOLO v11, LSTM, Risk Assessment, Precision Agriculture, Image Based Detection.},
        month = {March},
        }