Precision Agriculture Lecture 05 | Farm Data, Crop Models & AI Decisions

Learn how farm observations become model outputs and useful recommendations. The lecture covers crop-process models and machine learning, with particular attention to data leakage, realistic validation and the cost of a wrong decision. Topics covered: Data context, missing values, calibration and feature design Crop growth, thermal time and soil-water balance APSIM, DSSAT, WOFOST and state estimation Regression, random forests and computer vision Precision, recall, residuals and spatial or temporal validation Uncertainty, economic action thresholds and deployment audits Examples include Rice Crop Manager, GEOCIF, CropHarvest and a class exercise on interpreting 95% accuracy. Lecture 05 in the Precision Agriculture Lecture series. Instructor: Redmond R. Shamshiri Adaptive AgroTech https://adaptiveagrotech.com #PrecisionAgriculture #SmartFarming #AdaptiveAgroTech CHAPTERS 00:00 Start with the decision and trustworthy data 12:17 Crop-process models and water balance 25:26 Machine learning and prediction errors 37:03 Validation across farms and seasons 41:54 Uncertainty and decision thresholds 46:59 Decision-support cases and deployment