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Remote sensing technology has become a promising tool in yield prediction.Most prior work employs satellite imagery for county-level corn yieldprediction by spatially aggregating all pixels within a county into a singlevalue, potentially overlooking the detailed information and valuable insightsoffered by more granular data. To this end, this research examines each countyat the pixel level and applies multiple instance learning to leverage detailedinformation within a county. In addition, our method addresses the "mixedpixel" issue caused by the inconsistent resolution between feature datasets andcrop mask, which may introduce noise into the model and therefore hinderaccurate yield prediction. Specifically, the attention mechanism is employed toautomatically assign weights to different pixels, which can mitigate theinfluence of mixed pixels. The experimental results show that the developedmodel outperforms four other machine learning models over the past five yearsin the U.S. corn belt and demonstrates its best performance in 2022, achievinga 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 fromboth spatial and temporal perspectives. Furthermore, through an in-depth studyof the relationship between mixed pixels and attention, it is verified that ourapproach can capture critical feature information while filtering out noisefrom mixed pixels.

Learning county from pixels: Corn yield prediction with attention-weighted multiple instance learning