  In the U.S., corn is the most produced crop and has been an essential part of
the American diet. To meet the demand for supply chain management and regional
food security, accurate and timely large-scale corn yield prediction is
attracting more attention in precision agriculture. Recently, remote sensing
technology and machine learning methods have been widely explored for crop
yield prediction. Currently, most county-level yield prediction models use
county-level mean variables for prediction, ignoring much detailed information.
Moreover, inconsistent spatial resolution between crop area and satellite
sensors results in mixed pixels, which may decrease the prediction accuracy.
Only a few works have addressed the mixed pixels problem in large-scale crop
yield prediction. To address the information loss and mixed pixels problem, we
developed a variational autoencoder (VAE) based multiple instance regression
(MIR) model for large-scaled corn yield prediction. We use all unlabeled data
to train a VAE and the well-trained VAE for anomaly detection. As a preprocess
method, anomaly detection can help MIR find a better representation of every
bag than traditional MIR methods, thus better performing in large-scale corn
yield prediction. Our experiments showed that variational autoencoder based
multiple instance regression (VAEMIR) outperformed all baseline methods in
large-scale corn yield prediction. Though a suitable meta parameter is
required, VAEMIR shows excellent potential in feature learning and extraction
for large-scale corn yield prediction.
