On Convolutional Vision Transformers for Yield Prediction
Alvin Inderka Florian Huber Volker Steinhage
While a variety of methods offer good yield prediction on histogrammed remote
sensing data, vision Transformers are only sparsely represented in the
literature. The Convolution vision Transformer (CvT) is being tested to
evaluate vision Transformers that are currently achieving state-of-the-art
results in many other vision tasks. CvT combines some of the advantages of
convolution with the advantages of dynamic attention and global context fusion
of Transformers. It performs worse than widely tested methods such as XGBoost
and CNNs, but shows that Transformers have potential to improve yield
prediction.