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@article{199204,
        author = {Farha Naaz and G.R.Deeksha and Kavyashree.K and Sanjana.S.Marnur},
        title = {AI Based Crop Health Monitoring Using NDVI Time-Series Derived from Satellite Spectral Data.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15573-15578},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199204},
        abstract = {Precision agriculture requires intelligent and scalable systems to monitor crop conditions and improve productivity. This paper presents an AI-based crop health monitoring and yield prediction framework using NDVI (Normalized Difference Vegetation Index) time-series derived from satellite spectral data. The system utilizes Sentinel-2 satellite imagery to analyze vegetation patterns across the Kharif season and assess crop conditions at different growth stages.
Unlike conventional approaches that rely solely on vegetation indices, the proposed system incorporates sowing date information to perform stage-aware crop health analysis. By evaluating NDVI values in relation to crop growth stages, the system accurately distinguishes between early-stage crops and stressed vegetation, thereby improving reliability in real-world scenarios. Machine learning models are further employed to predict crop yield during later stages of the season, with prediction accuracy improving as more temporal data becomes available.
The system follows a client–server architecture, where the frontend provides an intuitive dashboard for monitoring crop condition, production risk, and expected yield, while the backend handles data processing and predictive modeling. Communication between components is achieved through RESTful APIs. The proposed framework enables near real-time crop monitoring and supports data-driven decision-making for agricultural management.},
        keywords = {Precision Agriculture, Crop Health Monitoring, NDVI Time-Series, Satellite Remote Sensing, Sentinel-2, Vegetation Analysis, Growth Stage Analysis, Yield Prediction, Machine Learning, Time-Series Analysis, Crop Monitoring System, RESTful APIs, Web-Based Dashboard.},
        month = {April},
        }