Plant phenomics & precision agriculture simulation of winter wheat growth by the assimilation of unmanned aerial vehicle imagery into the WOFOST model.

The aim of this study is to optimize the simulation result of the WOFOST model and explore the possibility of assimilating unmanned aerial vehicle (UAV) imagery into this model. Field images of wheat during its key growth stages are acquired with a UAV, and the corresponding leaf area index (LAI), b...

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Autores principales:	Tianle Yang, Weijun Zhang, Tong Zhou, Wei Wu, Tao Liu, Chengming Sun
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2f2223d7faa04609b0d85238dd222009
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id	oai:doaj.org-article:2f2223d7faa04609b0d85238dd222009
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spelling	oai:doaj.org-article:2f2223d7faa04609b0d85238dd2220092021-12-02T20:13:45ZPlant phenomics & precision agriculture simulation of winter wheat growth by the assimilation of unmanned aerial vehicle imagery into the WOFOST model.1932-620310.1371/journal.pone.0246874https://doaj.org/article/2f2223d7faa04609b0d85238dd2220092021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0246874https://doaj.org/toc/1932-6203The aim of this study is to optimize the simulation result of the WOFOST model and explore the possibility of assimilating unmanned aerial vehicle (UAV) imagery into this model. Field images of wheat during its key growth stages are acquired with a UAV, and the corresponding leaf area index (LAI), biomass, and final yield are experimentally measured. LAI data is retrieved from the UAV imagery and assimilated into a localized WOFOST model using least squares optimization. Sensitive parameters, i.e., specific leaf area (SLATB0, SLATB0.5, SLATB2) and maximum CO2 assimilation rate (AMAXTB1, AMAXTB1.3) are adjusted to minimize the discrepancy between the LAI obtained from the model simulation and inversion of the UAV data. The results show that the assimilated model provides a better estimation of the growth and development of winter wheat in the study area. The R2, RMSE, and NRMSE of winter wheat LAI simulated with the assimilated WOFOST model are 0.8812, 0.49, and 23.5% respectively. The R2, RMSE, and NRMSE of the simulated yield are 0.9489, 327.06 kg·hm-2, and 6.5%. The accuracy in model simulation of winter wheat growth is improved, which demonstrates the feasibility of integrating UAV data into crop models.Tianle YangWeijun ZhangTong ZhouWei WuTao LiuChengming SunPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0246874 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tianle Yang Weijun Zhang Tong Zhou Wei Wu Tao Liu Chengming Sun Plant phenomics & precision agriculture simulation of winter wheat growth by the assimilation of unmanned aerial vehicle imagery into the WOFOST model.
description	The aim of this study is to optimize the simulation result of the WOFOST model and explore the possibility of assimilating unmanned aerial vehicle (UAV) imagery into this model. Field images of wheat during its key growth stages are acquired with a UAV, and the corresponding leaf area index (LAI), biomass, and final yield are experimentally measured. LAI data is retrieved from the UAV imagery and assimilated into a localized WOFOST model using least squares optimization. Sensitive parameters, i.e., specific leaf area (SLATB0, SLATB0.5, SLATB2) and maximum CO2 assimilation rate (AMAXTB1, AMAXTB1.3) are adjusted to minimize the discrepancy between the LAI obtained from the model simulation and inversion of the UAV data. The results show that the assimilated model provides a better estimation of the growth and development of winter wheat in the study area. The R2, RMSE, and NRMSE of winter wheat LAI simulated with the assimilated WOFOST model are 0.8812, 0.49, and 23.5% respectively. The R2, RMSE, and NRMSE of the simulated yield are 0.9489, 327.06 kg·hm-2, and 6.5%. The accuracy in model simulation of winter wheat growth is improved, which demonstrates the feasibility of integrating UAV data into crop models.
format	article
author	Tianle Yang Weijun Zhang Tong Zhou Wei Wu Tao Liu Chengming Sun
author_facet	Tianle Yang Weijun Zhang Tong Zhou Wei Wu Tao Liu Chengming Sun
author_sort	Tianle Yang
title	Plant phenomics & precision agriculture simulation of winter wheat growth by the assimilation of unmanned aerial vehicle imagery into the WOFOST model.
title_short	Plant phenomics & precision agriculture simulation of winter wheat growth by the assimilation of unmanned aerial vehicle imagery into the WOFOST model.
title_full	Plant phenomics & precision agriculture simulation of winter wheat growth by the assimilation of unmanned aerial vehicle imagery into the WOFOST model.
title_fullStr	Plant phenomics & precision agriculture simulation of winter wheat growth by the assimilation of unmanned aerial vehicle imagery into the WOFOST model.
title_full_unstemmed	Plant phenomics & precision agriculture simulation of winter wheat growth by the assimilation of unmanned aerial vehicle imagery into the WOFOST model.
title_sort	plant phenomics & precision agriculture simulation of winter wheat growth by the assimilation of unmanned aerial vehicle imagery into the wofost model.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/2f2223d7faa04609b0d85238dd222009
work_keys_str_mv	AT tianleyang plantphenomicsprecisionagriculturesimulationofwinterwheatgrowthbytheassimilationofunmannedaerialvehicleimageryintothewofostmodel AT weijunzhang plantphenomicsprecisionagriculturesimulationofwinterwheatgrowthbytheassimilationofunmannedaerialvehicleimageryintothewofostmodel AT tongzhou plantphenomicsprecisionagriculturesimulationofwinterwheatgrowthbytheassimilationofunmannedaerialvehicleimageryintothewofostmodel AT weiwu plantphenomicsprecisionagriculturesimulationofwinterwheatgrowthbytheassimilationofunmannedaerialvehicleimageryintothewofostmodel AT taoliu plantphenomicsprecisionagriculturesimulationofwinterwheatgrowthbytheassimilationofunmannedaerialvehicleimageryintothewofostmodel AT chengmingsun plantphenomicsprecisionagriculturesimulationofwinterwheatgrowthbytheassimilationofunmannedaerialvehicleimageryintothewofostmodel
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Plant phenomics & precision agriculture simulation of winter wheat growth by the assimilation of unmanned aerial vehicle imagery into the WOFOST model.

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