Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data

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.

Authors

• Jiaxuan You
• Xiaocheng Li
• Melvin Low
• David Lobell Stanford University
• Stefano Ermon Stanford University

Keywords

No keywords are indexed for this paper.