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Wheat yield estimation using remote sensing data based on machine learning approaches

Accurate predictions of wheat yields are essential to farmers’production plans and to the international trade in wheat. However, only poor approximations of the productivity of wheat crops in China can be obtained using traditional linear regression models based on vegetation indices and observation...

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Autores principales: Cheng, Enhui, Zhang, Bing, Peng, Dailiang, Zhong, Liheng, Yu, Le, Liu, Yao, Xiao, Chenchao, Li, Cunjun, Li, Xiaoyi, Chen, Yue, Ye, Huichun, Wang, Hongye, Yu, Ruyi, Hu, Jinkang, Yang, Songlin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9816798/
https://www.ncbi.nlm.nih.gov/pubmed/36618627
http://dx.doi.org/10.3389/fpls.2022.1090970
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author Cheng, Enhui
Zhang, Bing
Peng, Dailiang
Zhong, Liheng
Yu, Le
Liu, Yao
Xiao, Chenchao
Li, Cunjun
Li, Xiaoyi
Chen, Yue
Ye, Huichun
Wang, Hongye
Yu, Ruyi
Hu, Jinkang
Yang, Songlin
author_facet Cheng, Enhui
Zhang, Bing
Peng, Dailiang
Zhong, Liheng
Yu, Le
Liu, Yao
Xiao, Chenchao
Li, Cunjun
Li, Xiaoyi
Chen, Yue
Ye, Huichun
Wang, Hongye
Yu, Ruyi
Hu, Jinkang
Yang, Songlin
author_sort Cheng, Enhui
collection PubMed
description Accurate predictions of wheat yields are essential to farmers’production plans and to the international trade in wheat. However, only poor approximations of the productivity of wheat crops in China can be obtained using traditional linear regression models based on vegetation indices and observations of the yield. In this study, Sentinel-2 (multispectral data) and ZY-1 02D (hyperspectral data) were used together with 15709 gridded yield data (with a resolution of 5 m × 5 m) to predict the winter wheat yield. These estimates were based on four mainstream data-driven approaches: Long Short-Term Memory (LSTM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Regression (SVR). The method that gave the best estimate of the winter wheat yield was determined, and the accuracy of the estimates based on multispectral and hyperspectral data were compared. The results showed that the LSTM model, for which the RMSE of the estimates was 0.201 t/ha, performed better than the RF (RMSE = 0.260 t/ha), GBDT (RMSE = 0.306 t/ha), and SVR (RMSE = 0.489 t/ha) methods. The estimates based on the ZY-1 02D hyperspectral data were more accurate than those based on the 30-m Sentinel-2 data: RMSE = 0.237 t/ha for the ZY-1 02D data, which is about a 5% improvement on the RSME of 0.307 t/ha for the 30-m Sentinel-2 data. However, the 10-m Sentinel-2 data performed even better, giving an RMSE of 0.219 t/ha. In addition, it was found that the greenness vegetation index SR (simple ratio index) outperformed the traditional vegetation indices. The results highlight the potential of the shortwave infrared bands to replace the visible and near-infrared bands for predicting crop yields Our study demonstrates the advantages of the deep learning method LSTM over machine learning methods in terms of its ability to make accurate estimates of the winter wheat yield.
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spelling pubmed-98167982023-01-07 Wheat yield estimation using remote sensing data based on machine learning approaches Cheng, Enhui Zhang, Bing Peng, Dailiang Zhong, Liheng Yu, Le Liu, Yao Xiao, Chenchao Li, Cunjun Li, Xiaoyi Chen, Yue Ye, Huichun Wang, Hongye Yu, Ruyi Hu, Jinkang Yang, Songlin Front Plant Sci Plant Science Accurate predictions of wheat yields are essential to farmers’production plans and to the international trade in wheat. However, only poor approximations of the productivity of wheat crops in China can be obtained using traditional linear regression models based on vegetation indices and observations of the yield. In this study, Sentinel-2 (multispectral data) and ZY-1 02D (hyperspectral data) were used together with 15709 gridded yield data (with a resolution of 5 m × 5 m) to predict the winter wheat yield. These estimates were based on four mainstream data-driven approaches: Long Short-Term Memory (LSTM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Regression (SVR). The method that gave the best estimate of the winter wheat yield was determined, and the accuracy of the estimates based on multispectral and hyperspectral data were compared. The results showed that the LSTM model, for which the RMSE of the estimates was 0.201 t/ha, performed better than the RF (RMSE = 0.260 t/ha), GBDT (RMSE = 0.306 t/ha), and SVR (RMSE = 0.489 t/ha) methods. The estimates based on the ZY-1 02D hyperspectral data were more accurate than those based on the 30-m Sentinel-2 data: RMSE = 0.237 t/ha for the ZY-1 02D data, which is about a 5% improvement on the RSME of 0.307 t/ha for the 30-m Sentinel-2 data. However, the 10-m Sentinel-2 data performed even better, giving an RMSE of 0.219 t/ha. In addition, it was found that the greenness vegetation index SR (simple ratio index) outperformed the traditional vegetation indices. The results highlight the potential of the shortwave infrared bands to replace the visible and near-infrared bands for predicting crop yields Our study demonstrates the advantages of the deep learning method LSTM over machine learning methods in terms of its ability to make accurate estimates of the winter wheat yield. Frontiers Media S.A. 2022-12-23 /pmc/articles/PMC9816798/ /pubmed/36618627 http://dx.doi.org/10.3389/fpls.2022.1090970 Text en Copyright © 2022 Cheng, Zhang, Peng, Zhong, Yu, Liu, Xiao, Li, Li, Chen, Ye, Wang, Yu, Hu and Yang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Cheng, Enhui
Zhang, Bing
Peng, Dailiang
Zhong, Liheng
Yu, Le
Liu, Yao
Xiao, Chenchao
Li, Cunjun
Li, Xiaoyi
Chen, Yue
Ye, Huichun
Wang, Hongye
Yu, Ruyi
Hu, Jinkang
Yang, Songlin
Wheat yield estimation using remote sensing data based on machine learning approaches
title Wheat yield estimation using remote sensing data based on machine learning approaches
title_full Wheat yield estimation using remote sensing data based on machine learning approaches
title_fullStr Wheat yield estimation using remote sensing data based on machine learning approaches
title_full_unstemmed Wheat yield estimation using remote sensing data based on machine learning approaches
title_short Wheat yield estimation using remote sensing data based on machine learning approaches
title_sort wheat yield estimation using remote sensing data based on machine learning approaches
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9816798/
https://www.ncbi.nlm.nih.gov/pubmed/36618627
http://dx.doi.org/10.3389/fpls.2022.1090970
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