Article

Deep Gaussian process for crop yield prediction based on remote sensing data

Authors:

David Lobell, and

Stefano ErmonAuthors Info & Claims

AAAI'17: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence

February 2017

Pages 4559 - 4565

Published: 04 February 2017 Publication History

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.

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Zhang CCai LChen MLi XCong G(2024)DeepMeshCity: A Deep Learning Model for Urban Grid PredictionACM Transactions on Knowledge Discovery from Data10.1145/365285918:6(1-26)Online publication date: 29-Apr-2024
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Sambasivan NVeeraraghavan R(2022)The Deskilling of Domain Expertise in AI DevelopmentProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517578(1-14)Online publication date: 29-Apr-2022
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Rolnick DDonti PKaack LKochanski KLacoste ASankaran KRoss AMilojevic-Dupont NJaques NWaldman-Brown ALuccioni AMaharaj TSherwin EMukkavilli SKording KGomes CNg AHassabis DPlatt JCreutzig FChayes JBengio Y(2022)Tackling Climate Change with Machine LearningACM Computing Surveys10.1145/348512855:2(1-96)Online publication date: 7-Feb-2022
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Deep Gaussian process for crop yield prediction based on remote sensing data
1. Applied computing
  1. Physical sciences and engineering
    1. Earth and atmospheric sciences

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cover image Guide Proceedings

AAAI'17: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence

February 2017

5106 pages

Program Chairs:
Satinder Singh
University of Michigan
,
Shaul Markovitch
Technion-Israel Institute of Technology

Sponsors

Association for the Advancement of Artificial Intelligence
amazon: amazon
Infosys
Facebook: Facebook
IBM: IBM

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AAAI Press

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Published: 04 February 2017

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Cited By

Zhang CCai LChen MLi XCong G(2024)DeepMeshCity: A Deep Learning Model for Urban Grid PredictionACM Transactions on Knowledge Discovery from Data10.1145/365285918:6(1-26)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3652859
Sambasivan NVeeraraghavan R(2022)The Deskilling of Domain Expertise in AI DevelopmentProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517578(1-14)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517578
Rolnick DDonti PKaack LKochanski KLacoste ASankaran KRoss AMilojevic-Dupont NJaques NWaldman-Brown ALuccioni AMaharaj TSherwin EMukkavilli SKording KGomes CNg AHassabis DPlatt JCreutzig FChayes JBengio Y(2022)Tackling Climate Change with Machine LearningACM Computing Surveys10.1145/348512855:2(1-96)Online publication date: 7-Feb-2022
https://dl.acm.org/doi/10.1145/3485128
Das MGhosh SDey LChaudhury SKrishnapuram RSingla PRoy R(2019)Space-time Prediction of High Resolution Raster DataProceedings of the ACM India Joint International Conference on Data Science and Management of Data10.1145/3297001.3297017(129-135)Online publication date: 3-Jan-2019
https://dl.acm.org/doi/10.1145/3297001.3297017
Jean NXie SErmon S(2018)Semi-supervised deep kernel learningProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3327345.3327438(5327-5338)Online publication date: 3-Dec-2018
https://dl.acm.org/doi/10.5555/3327345.3327438

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