AI is enabling precision agriculture that reduces costs while increasing yields. Key applications: 1) Crop monitoring with satellite imagery (Sentinel-2, Planet Labs) analyzed by CNN models to detect NDVI (vegetation index) anomalies indicating stress, disease, or water deficiency - identify issues 2 weeks before visible to human eye. 2) Disease and pest detection: computer vision models trained on leaf images achieving 95%+ accuracy on common crop diseases, enabling targeted pesticide application (30-50% reduction). 3) Yield prediction: ensemble models combining weather forecasts, soil data, historical yields, and satellite imagery to predict harvest 3 months in advance with 90% accuracy. 4) Smart irrigation: soil moisture sensors + weather APIs + evapotranspiration models to optimize watering timing and volume, reducing water use by 30-50%. 5) Automated harvesting: robotics + computer vision for selective harvesting of ripe produce (strawberries, tomatoes). 6) Supply chain optimization: yield predictions fed into logistics planning for fresher delivery. ROI: AI-driven precision agriculture reduces input costs 15-20% and increases yields 5-10%.