Crop yield prediction utilizing multimodal deep learning

Precision agriculture is a vital practice for improving the production of crops. The present work is aimed to develop a multimodal deep learning model that is able to produce a prediction map of the health of crops. The model takes multispectral images and field sensor data (humidity, temperature, soil status, etc.) as an input and creates a yield map of a crop. The utilization of multimodal data is aimed to extract hidden patterns in the status of crops and in this way obtain better results than the use of vegetation indices.