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== <span style="color: #FFFFFF;">Creating</span> == Designing a crop monitoring AI system: (1) Data: acquire multitemporal Sentinel-2 satellite imagery (free, 10m resolution) for target region + historical yield records. (2) Feature engineering: compute NDVI, EVI, LAI time series; extract growing degree days and precipitation from weather API. (3) Yield model: XGBoost trained on (satellite features, weather, soil) β yield. (4) Disease alert: CNN deployed on mobile app for field scouting; triggered by satellite anomaly detection. (5) Dashboard: farmer-facing map of field zones with yield predictions, disease alerts, irrigation recommendations. (6) Feedback loop: collect harvest ground truth to retrain yield model annually. [[Category:Artificial Intelligence]] [[Category:Agriculture]] [[Category:Computer Vision]] </div>
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