Multilevel Deep Learning Network for County-Level Corn Yield Estimation in the U.S. Corn Belt
Accurate and timely estimation of crop yield at a small scale is of great significance to food security and harvest management. Recent studies have proven remote sensing is an efficient method for yield estimation and machine learning, especially deep learning, can infer a good prediction by integra...
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doaj-f5f2df4dc47c4f2ab8a0b693fd41199c2021-06-03T23:06:54ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352020-01-01135048506010.1109/JSTARS.2020.30190469177261Multilevel Deep Learning Network for County-Level Corn Yield Estimation in the U.S. Corn BeltJie Sun0https://orcid.org/0000-0002-5125-9149Zulong Lai1Liping Di2https://orcid.org/0000-0002-3953-9965Ziheng Sun3Jianbin Tao4Yonglin Shen5https://orcid.org/0000-0001-8190-4615School of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaCenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USACenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USADepartment of Geographic Information Sciences, Central China Normal University, Wuhan, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaAccurate and timely estimation of crop yield at a small scale is of great significance to food security and harvest management. Recent studies have proven remote sensing is an efficient method for yield estimation and machine learning, especially deep learning, can infer a good prediction by integrating multisource datasets such as satellite data, climate data, soil data, and so on. However, there are some bottleneck challenges to improve accuracy. First, the popular remote sensing data used for yield prediction fall into two major groups-time-series data and constant data. Surprisingly little attention has been devoted to deep learning networks which can integrate the two kinds of data effectively; second, both temporal and spatial features play a role in affecting the yields. But most of the existing approaches employed either convolutional neural network (CNN) or recurrent neural network (RNN). CNN cannot learn temporal patterns, while RNN barely can learn spatial characteristics. This work proposed a novel multilevel deep learning model coupling RNN and CNN to extract both spatial and temporal features. The inputs include both time-series remote sensing data, soil property data, and the model outputs yield. We experimented with the model in U.S. Corn Belt states, and used it to predict corn yield from 2013 to 2016 at the county-level. The results approve the effectiveness and advantages of the proposed approach over the other methods. In the future, the model will be used on other crops such as soybean and winter wheat to assist agricultural decision-making.https://ieeexplore.ieee.org/document/9177261/Convolutional neural network (CNN)county-levellong short-term memory (LSTM)predictionyield |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jie Sun Zulong Lai Liping Di Ziheng Sun Jianbin Tao Yonglin Shen |
spellingShingle |
Jie Sun Zulong Lai Liping Di Ziheng Sun Jianbin Tao Yonglin Shen Multilevel Deep Learning Network for County-Level Corn Yield Estimation in the U.S. Corn Belt IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Convolutional neural network (CNN) county-level long short-term memory (LSTM) prediction yield |
author_facet |
Jie Sun Zulong Lai Liping Di Ziheng Sun Jianbin Tao Yonglin Shen |
author_sort |
Jie Sun |
title |
Multilevel Deep Learning Network for County-Level Corn Yield Estimation in the U.S. Corn Belt |
title_short |
Multilevel Deep Learning Network for County-Level Corn Yield Estimation in the U.S. Corn Belt |
title_full |
Multilevel Deep Learning Network for County-Level Corn Yield Estimation in the U.S. Corn Belt |
title_fullStr |
Multilevel Deep Learning Network for County-Level Corn Yield Estimation in the U.S. Corn Belt |
title_full_unstemmed |
Multilevel Deep Learning Network for County-Level Corn Yield Estimation in the U.S. Corn Belt |
title_sort |
multilevel deep learning network for county-level corn yield estimation in the u.s. corn belt |
publisher |
IEEE |
series |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
issn |
2151-1535 |
publishDate |
2020-01-01 |
description |
Accurate and timely estimation of crop yield at a small scale is of great significance to food security and harvest management. Recent studies have proven remote sensing is an efficient method for yield estimation and machine learning, especially deep learning, can infer a good prediction by integrating multisource datasets such as satellite data, climate data, soil data, and so on. However, there are some bottleneck challenges to improve accuracy. First, the popular remote sensing data used for yield prediction fall into two major groups-time-series data and constant data. Surprisingly little attention has been devoted to deep learning networks which can integrate the two kinds of data effectively; second, both temporal and spatial features play a role in affecting the yields. But most of the existing approaches employed either convolutional neural network (CNN) or recurrent neural network (RNN). CNN cannot learn temporal patterns, while RNN barely can learn spatial characteristics. This work proposed a novel multilevel deep learning model coupling RNN and CNN to extract both spatial and temporal features. The inputs include both time-series remote sensing data, soil property data, and the model outputs yield. We experimented with the model in U.S. Corn Belt states, and used it to predict corn yield from 2013 to 2016 at the county-level. The results approve the effectiveness and advantages of the proposed approach over the other methods. In the future, the model will be used on other crops such as soybean and winter wheat to assist agricultural decision-making. |
topic |
Convolutional neural network (CNN) county-level long short-term memory (LSTM) prediction yield |
url |
https://ieeexplore.ieee.org/document/9177261/ |
work_keys_str_mv |
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1721398629214715904 |