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

作者：Jiaxuan You, Xiaocheng Li, Melvin Low, David B. Lobell, Stefano Ermon · 发表于：Proceedings of the AAAI Conference on Artificial Intelligence · 年份：2017 · DOI：10.1609/aaai.v31i1.11172 · 被引用次数：568 · 研究领域：Smart Agriculture and AI、Remote Sensing in Agriculture、Gaussian Processes and Bayesian Inference

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.