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

By Jiaxuan You, Xiaocheng Li, Melvin Low, David B. Lobell, Stefano Ermon. AAAI Conference on Artificial Intelligence, 2017

601 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/84289c5530d65e843f059c5dc0d251b3100ab89b

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Citations in the corpus

A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach (2018, background): “A recent study also found the potential use of SWIR in crop yield estimation (You et al., 2017).”
Tackling Climate Change with Machine Learning (2019, background): “amounts of water while reducing pests that thrive under excessive moisture [404]. ML can also help in disease detection, weed detection, and soil sensing [412–414]. ML can guide crop yield prediction [415] and even macroeconomic models that help farmers predict crop demand and decide what to plant a”
A Review of Deep Learning Models for Time Series Prediction (2019, background): “Besides, remote sensing is also one of the hot topics for the real application of CNN [80].”
County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model (2019, methodology): “It is important to highlight that the CNN and LSTM models can obtain their best prediction results in OCT (RMSE = 348.36) and NOV (RMSE = 353.07); in contrast, their best prediction results can be nearly achieved on AUG 21st (RMSE = 353.74) by the proposed CNN-LSTM model, which can win a long time i”
Deep learning in environmental remote sensing: Achievements and challenges (2020, methodology): “You et al. (2017) used CNN and LSTM to discover relevant features from raw data automatically and then placed a Gaussian process layer on top of the neural architectures to account for spatial and temporal dependencies across data points explicitly.”
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, methodology): “In global change analysis, DL models show advantages in estimating crop yield (Kuwata & Shibasaki, 2015; You et al., 2017).”
Aligning artificial intelligence with climate change mitigation (2022, methodology): “By learning from time series, ML has been used to forecast renewable power production 79 , crop yields 80 and transportation demands 81 .”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, neutral): “…world includes not only poverty mapping but also tracking child mortality (Osgood-Zimmerman et al., 2018; Reiner et al., 2018; Burke et al., 2016), educational attainment (Graetz et al., 2018), food security and crop yield prediction (Wang et al., 2020; You et al., 2017; Xie et al., 2020).”
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery (2022, background): “Moreover, automatic analysis of satellite imagery is often needed for tasks with large societal impact such as poverty or crop yield prediction [20, 21, 22, 23, 24, 25, 26, 27, 28], where acquiring large amounts of labeled data through surveys is impossible or prohibitively expensive.”
Machine learning for large-scale crop yield forecasting (2020, methodology): “Previous studies (e.g. Shahhosseini et al. 2019; Cai et al. 2019; You et al. 2017; Jeong et al. 2016) have demonstrated that machine learning can play an important role in crop yield prediction and the same was confirmed by our results.”
A CNN-RNN Framework for Crop Yield Prediction (2019, background): “You et al. (2017) applied CNNs and RNNs to predict soybean yield based on a sequence of remotely sensed images.”