Predicting Crop Yield With Machine Learning: An Extensive Analysis Of
  Input Modalities And Models On a Field and sub-field Level
Deepak Pathak Miro Miranda Francisco Mena Cristhian Sanchez Patrick Helber Benjamin Bischke Peter Habelitz Hiba Najjar Jayanth Siddamsetty Diego Arenas Michaela Vollmer Marcela Charfuelan Marlon Nuske Andreas Dengel
We introduce a simple yet effective early fusion method for crop yield
prediction that handles multiple input modalities with different temporal and
spatial resolutions. We use high-resolution crop yield maps as ground truth
data to train crop and machine learning model agnostic methods at the sub-field
level. We use Sentinel-2 satellite imagery as the primary modality for input
data with other complementary modalities, including weather, soil, and DEM
data. The proposed method uses input modalities available with global coverage,
making the framework globally scalable. We explicitly highlight the importance
of input modalities for crop yield prediction and emphasize that the
best-performing combination of input modalities depends on region, crop, and
chosen model.