On Convolutional Vision Transformers for Yield Prediction
This is an incremental study for agricultural yield prediction, showing limited immediate impact.
The paper tackled yield prediction using remote sensing data by testing the Convolution vision Transformer (CvT), which performed worse than established methods like XGBoost and CNNs but demonstrated potential for improvement.
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.