Learning to Forecast Crop Growth from Earth Observation Data
Forecasting crop growth across agricultural landscapes is important for improving the productivity, resilience, and operational management of farming systems. In this work, we investigate whether Earth observation time series and meteorological drivers can be used to predict future canopy development at country scale. We focus on winter wheat and formulate crop growth prediction as forecasting future leaf area index (LAI) trajectories beyond the last available Sentinel-2 observation. We evaluate this task on a multi-year dataset which spans the entire country of Switzerland, containing over 20
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- PossiblePossibly related (embedding) · 53%Regional drought prediction from Sentinel-2 time series using Random Forest, DNN, and 1D-CNN: a case study in Marchfeld, Austria - EurekAlert! →
- PossiblePossibly related (embedding) · 48%Agriculture is ready for AI, but its data isn’t →
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Learning to Forecast Crop Growth from Earth Observation Data” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Dominik Senti →
“Learning to Forecast Crop Growth from Earth Observation Data”
- LinkedLinked via arxiv author · 85%Mehmet Ozgur Turkoglu →
“Learning to Forecast Crop Growth from Earth Observation Data”
- LinkedLinked via arxiv author · 85%Michele Volpi →
“Learning to Forecast Crop Growth from Earth Observation Data”
- LinkedLinked via arxiv author · 85%Helge Aasen →
“Learning to Forecast Crop Growth from Earth Observation Data”