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.

利用多视角学习方法和多视图门控融合模型 (MVGF)，本研究预测不同作物和地区的农作物产量，数据源包括 Sentinel-2 卫星的多光谱光学图像和作物生长季节的气象数据，以及土壤特性和地形信息等静态特征，实验结果表明 MVGF 模型相较于传统模型在预测任务上具有更好的表现，达到了 0.68 的 R2 值。