Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field
  Crop Yield Prediction
Francisco Mena Deepak Pathak Hiba Najjar Cristhian Sanchez Patrick Helber Benjamin Bischke Peter Habelitz Miro Miranda Jayanth Siddamsetty Marlon Nuske Marcela Charfuelan Diego Arenas Michaela Vollmer Andreas Dengel
Accurate crop yield prediction is of utmost importance for informed
decision-making in agriculture, aiding farmers, and industry stakeholders.
However, this task is complex and depends on multiple factors, such as
environmental conditions, soil properties, and management practices. Combining
heterogeneous data views poses a fusion challenge, like identifying the
view-specific contribution to the predictive task. We present a novel
multi-view learning approach to predict crop yield for different crops
(soybean, wheat, rapeseed) and regions (Argentina, Uruguay, and Germany). Our
multi-view input data includes multi-spectral optical images from Sentinel-2
satellites and weather data as dynamic features during the crop growing season,
complemented by static features like soil properties and topographic
information. To effectively fuse the data, we introduce a Multi-view Gated
Fusion (MVGF) model, comprising dedicated view-encoders and a Gated Unit (GU)
module. The view-encoders handle the heterogeneity of data sources with varying
temporal resolutions by learning a view-specific representation. These
representations are adaptively fused via a weighted sum. The fusion weights are
computed for each sample by the GU using a concatenation of the
view-representations. The MVGF model is trained at sub-field level with 10 m
resolution pixels. Our evaluations show that the MVGF outperforms conventional
models on the same task, achieving the best results by incorporating all the
data sources, unlike the usual fusion results in the literature. For Argentina,
the MVGF model achieves an R2 value of 0.68 at sub-field yield prediction,
while at field level evaluation (comparing field averages), it reaches around
0.80 across different countries. The GU module learned different weights based
on the country and crop-type, aligning with the variable significance of each
data source to the prediction task.