Remote sensing technology has become a promising tool in yield prediction.
Most prior work employs satellite imagery for county-level corn yield
prediction by spatially aggregating all pixels within a county into a single
value, potentially overlooking the detailed information and valuable insights
offered by more granular data. To this end, this research examines each county
at the pixel level and applies multiple instance learning to leverage detailed
information within a county. In addition, our method addresses the "mixed
pixel" issue caused by the inconsistent resolution between feature datasets and
crop mask, which may introduce noise into the model and therefore hinder
accurate yield prediction. Specifically, the attention mechanism is employed to
automatically assign weights to different pixels, which can mitigate the
influence of mixed pixels. The experimental results show that the developed
model outperforms four other machine learning models over the past five years
in the U.S. corn belt and demonstrates its best performance in 2022, achieving
a coefficient of determination (R2) value of 0.84 and a root mean square error
(RMSE) of 0.83. This paper demonstrates the advantages of our approach from
both spatial and temporal perspectives. Furthermore, through an in-depth study
of the relationship between mixed pixels and attention, it is verified that our
approach can capture critical feature information while filtering out noise
from mixed pixels.

遥感技术在产量预测中已成为一种有前途的工具，本研究运用多实例学习在像素级别对每个县进行研究，利用细粒度数据的详细信息，并通过引入注意机制来解决特征数据集和作物掩膜之间分辨率不一致导致的混合像素问题，实验结果表明该模型在美国玉米带的过去五年中优于其他四个机器学习模型，并在 2022 年达到最佳性能，R2 值为 0.84，RMSE 为 0.83，进一步通过对混合像素和注意力之间关系的深入研究验证了该方法能够捕捉关键特征信息并过滤混合像素中的噪声。