Agricultural monitoring, especially in developing countries,
can help prevent famine and support humanitarian efforts. A central challenge is yield estimation, i.e., predicting crop
yields before harvest...We introduce a scalable, accurate, and inexpensive method
to predict crop yields using publicly available remote sensing data. Our approach improves existing techniques in three
ways. First, we forego hand-crafted features traditionally
used in the remote sensing community and propose an approach based on modern representation learning ideas. We
also introduce a novel dimensionality reduction technique
that allows us to train a Convolutional Neural Network or
Long-short Term Memory network and automatically learn
useful features even when labeled training data are scarce. Finally, we incorporate a Gaussian Process component to
explicitly model the spatio-temporal structure of the data
and further improve accuracy. We evaluate our approach on
county-level soybean yield prediction in the U.S. and show
that it outperforms competing techniques.(read more)