Cargando…

Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning

A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. To address this problem, this research investigated a conceptual framework by integrating a crop growth model with a radiative transfer model to int...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Qiaomin, Zheng, Bangyou, Chen, Tong, Chapman, Scott C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629788/
https://www.ncbi.nlm.nih.gov/pubmed/35768163
http://dx.doi.org/10.1093/jxb/erac291
_version_ 1784823468213141504
author Chen, Qiaomin
Zheng, Bangyou
Chen, Tong
Chapman, Scott C
author_facet Chen, Qiaomin
Zheng, Bangyou
Chen, Tong
Chapman, Scott C
author_sort Chen, Qiaomin
collection PubMed
description A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. To address this problem, this research investigated a conceptual framework by integrating a crop growth model with a radiative transfer model to introduce biological constraints in a synthetic training dataset. In addition to the comparison of two datasets without and with biological constraints, we also investigated the effects of observation geometry, retrieval method, and wavelength range on estimation accuracy of four crop traits (leaf area index, leaf chlorophyll content, leaf dry matter, and leaf water content) of wheat. The theoretical analysis demonstrated potential advantages of adding biological constraints in synthetic training datasets as well as the capability of deep learning. Additionally, the predictive models were validated on real unmanned aerial vehicle-based multispectral images collected from wheat plots contrasting in canopy structure. The predictive model trained over a synthetic dataset with biological constraints enabled the prediction of leaf water content from using wavelengths in the visible to near infrared range based on the correlations between crop traits. Our findings presented the potential of the proposed conceptual framework in simultaneously retrieving multiple crop traits from canopy reflectance for applications in precision agriculture and plant breeding.
format Online
Article
Text
id pubmed-9629788
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-96297882022-11-04 Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning Chen, Qiaomin Zheng, Bangyou Chen, Tong Chapman, Scott C J Exp Bot Technical Innovations A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. To address this problem, this research investigated a conceptual framework by integrating a crop growth model with a radiative transfer model to introduce biological constraints in a synthetic training dataset. In addition to the comparison of two datasets without and with biological constraints, we also investigated the effects of observation geometry, retrieval method, and wavelength range on estimation accuracy of four crop traits (leaf area index, leaf chlorophyll content, leaf dry matter, and leaf water content) of wheat. The theoretical analysis demonstrated potential advantages of adding biological constraints in synthetic training datasets as well as the capability of deep learning. Additionally, the predictive models were validated on real unmanned aerial vehicle-based multispectral images collected from wheat plots contrasting in canopy structure. The predictive model trained over a synthetic dataset with biological constraints enabled the prediction of leaf water content from using wavelengths in the visible to near infrared range based on the correlations between crop traits. Our findings presented the potential of the proposed conceptual framework in simultaneously retrieving multiple crop traits from canopy reflectance for applications in precision agriculture and plant breeding. Oxford University Press 2022-06-30 /pmc/articles/PMC9629788/ /pubmed/35768163 http://dx.doi.org/10.1093/jxb/erac291 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Experimental Biology. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technical Innovations
Chen, Qiaomin
Zheng, Bangyou
Chen, Tong
Chapman, Scott C
Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning
title Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning
title_full Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning
title_fullStr Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning
title_full_unstemmed Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning
title_short Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning
title_sort integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning
topic Technical Innovations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629788/
https://www.ncbi.nlm.nih.gov/pubmed/35768163
http://dx.doi.org/10.1093/jxb/erac291
work_keys_str_mv AT chenqiaomin integratingacropgrowthmodelandradiativetransfermodeltoimproveestimationofcroptraitsbasedondeeplearning
AT zhengbangyou integratingacropgrowthmodelandradiativetransfermodeltoimproveestimationofcroptraitsbasedondeeplearning
AT chentong integratingacropgrowthmodelandradiativetransfermodeltoimproveestimationofcroptraitsbasedondeeplearning
AT chapmanscottc integratingacropgrowthmodelandradiativetransfermodeltoimproveestimationofcroptraitsbasedondeeplearning