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Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data

The heading and flowering stages are crucial for wheat growth and should be used for fusarium head blight (FHB) and other plant prevention operations. Rapid and accurate monitoring of wheat growth in hilly areas is critical for determining plant protection operations and strategies. Currently, the o...

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Autores principales: Li, Yibai, Cao, Guangqiao, Liu, Dong, Zhang, Jinlong, Li, Liang, Chen, Cong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9715324/
https://www.ncbi.nlm.nih.gov/pubmed/36465952
http://dx.doi.org/10.1155/2022/3655804
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author Li, Yibai
Cao, Guangqiao
Liu, Dong
Zhang, Jinlong
Li, Liang
Chen, Cong
author_facet Li, Yibai
Cao, Guangqiao
Liu, Dong
Zhang, Jinlong
Li, Liang
Chen, Cong
author_sort Li, Yibai
collection PubMed
description The heading and flowering stages are crucial for wheat growth and should be used for fusarium head blight (FHB) and other plant prevention operations. Rapid and accurate monitoring of wheat growth in hilly areas is critical for determining plant protection operations and strategies. Currently, the operation time for FHB prevention and plant protection is primarily determined by manual tour inspection of plant growth, which has the disadvantages of low information gathering and subjectivity. In this study, an unmanned aerial vehicle (UAV) equipped with a multispectral camera was used to collect wheat canopy multispectral images and heading rate information during the heading and flowering stages in order to develop a method for detecting the appropriate time for preventive control of FHB. A 1D convolutional neural network + decision tree model (1D CNN + DT) was designed. All the multispectral information was input into the model for feature extraction and result regression. The regression revealed that the coefficient of determination (R(2)) between multispectral information in the wheat canopy and the heading rate was 0.95, and the root mean square error of prediction (RMSE) was 0.24. This result was superior to that obtained by directly inputting multispectral data into neural networks (NN) or by inputting multispectral data into NN via traditional VI calculation, support vector machines regression (SVR), or decision tree (DT). On the basis of FHB prevention and control production guidelines and field research results, a discrimination model for FHB prevention and plant protection operation time was developed. After the output values of the regression model were input into the discrimination model, a 97.50% precision was obtained. The method proposed in this study can efficiently monitor the growth status of wheat during the heading and flowering stages and provide crop growth information for determining the timing and strategy of FHB prevention and plant protection operations.
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spelling pubmed-97153242022-12-02 Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data Li, Yibai Cao, Guangqiao Liu, Dong Zhang, Jinlong Li, Liang Chen, Cong Comput Intell Neurosci Research Article The heading and flowering stages are crucial for wheat growth and should be used for fusarium head blight (FHB) and other plant prevention operations. Rapid and accurate monitoring of wheat growth in hilly areas is critical for determining plant protection operations and strategies. Currently, the operation time for FHB prevention and plant protection is primarily determined by manual tour inspection of plant growth, which has the disadvantages of low information gathering and subjectivity. In this study, an unmanned aerial vehicle (UAV) equipped with a multispectral camera was used to collect wheat canopy multispectral images and heading rate information during the heading and flowering stages in order to develop a method for detecting the appropriate time for preventive control of FHB. A 1D convolutional neural network + decision tree model (1D CNN + DT) was designed. All the multispectral information was input into the model for feature extraction and result regression. The regression revealed that the coefficient of determination (R(2)) between multispectral information in the wheat canopy and the heading rate was 0.95, and the root mean square error of prediction (RMSE) was 0.24. This result was superior to that obtained by directly inputting multispectral data into neural networks (NN) or by inputting multispectral data into NN via traditional VI calculation, support vector machines regression (SVR), or decision tree (DT). On the basis of FHB prevention and control production guidelines and field research results, a discrimination model for FHB prevention and plant protection operation time was developed. After the output values of the regression model were input into the discrimination model, a 97.50% precision was obtained. The method proposed in this study can efficiently monitor the growth status of wheat during the heading and flowering stages and provide crop growth information for determining the timing and strategy of FHB prevention and plant protection operations. Hindawi 2022-11-24 /pmc/articles/PMC9715324/ /pubmed/36465952 http://dx.doi.org/10.1155/2022/3655804 Text en Copyright © 2022 Yibai Li et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Li, Yibai
Cao, Guangqiao
Liu, Dong
Zhang, Jinlong
Li, Liang
Chen, Cong
Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data
title Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data
title_full Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data
title_fullStr Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data
title_full_unstemmed Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data
title_short Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data
title_sort determination of wheat heading stage using convolutional neural networks on multispectral uav imaging data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9715324/
https://www.ncbi.nlm.nih.gov/pubmed/36465952
http://dx.doi.org/10.1155/2022/3655804
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