Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield Prediction

TL;DR

This paper introduces a novel multi-view learning model, MVGF, that adaptively fuses heterogeneous remote sensing and environmental data to improve crop yield prediction accuracy across different regions and crops.

Contribution

The paper presents the Multi-view Gated Fusion (MVGF) model, a new approach that effectively combines diverse data sources with view-specific encoders and a gating mechanism for enhanced yield prediction.

Findings

MVGF outperforms traditional models in yield prediction accuracy.

The model achieves an R2 of 0.68 at sub-field level and 0.80 at field level.

Gating weights adapt to crop type and country, reflecting data source importance.

Abstract

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.