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• W2604645045 abstract "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." @default.
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• W2604645045 date "2017-02-12" @default.
• W2604645045 modified "2023-10-11" @default.
• W2604645045 title "Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data" @default.
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• W2604645045 doi "https://doi.org/10.1609/aaai.v31i1.11172" @default.
• W2604645045 hasPublicationYear "2017" @default.
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