Learning county from pixels: Corn yield prediction with attention-weighted multiple instance learning
arxiv(2023)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要