  Increasing the accuracy of crop yield estimates may allow improvements in the
whole crop production chain, allowing farmers to better plan for harvest, and
for insurers to better understand risks of production, to name a few
advantages. To perform their predictions, most current machine learning models
use NDVI data, which can be hard to use, due to the presence of clouds and
their shadows in acquired images, and due to the absence of reliable crop masks
for large areas, especially in developing countries. In this paper, we present
a deep learning model able to perform pre-season and in-season predictions for
five different crops. Our model uses crop calendars, easy-to-obtain remote
sensing data and weather forecast information to provide accurate yield
estimates.
