A Machine Learning Framework for Crop Productivity Classification and Risk Assessment

Knowing in advance which fields are likely to yield poorly has obvious value for farm management, insurance, and logistics, yet reliable field-scale productivity classification and risk assessment from satellite data remain open problems. We present a machine learning framework for crop productivity classification and pre-harvest yield risk assessment for corn, soybean, and wheat in western Paraná, Brazil, integrating a harmonized Landsat/Sentinel-2 time-series with gridded climate variables and phenological features. The framework evaluates four algorithms on two tasks: three-class productivity classification (Low, Medium, High) and binary risk assessment (Low vs. Not Low), where a field flagged as Low is treated as at risk of poor yield before harvest. Early in the analysis, a single temporal feature, harvest_day_of_year, was found to account for 47–64% of model importance and to produce near-identical results across all five initially tested algorithms, a sign of near-deterministic separation rather than a genuine predictive signal. We excluded this feature and reported results on the remaining pre-harvest predictors only. For the binary risk assessment task, three algorithms achieved 84.5% accuracy for wheat and 74.9% for soybean using only pre-harvest features. For corn, ablation revealed that vegetation features contain genuine discriminative signal previously obscured: three algorithms now exceed the dummy baseline, versus only one before ablation. The wheat binary risk model shows strong application potential for early warning of yield risk; corn requires additional data, most likely more growing seasons covering a wider range of climate conditions, before it can be used reliably.