Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data Article Swipe
Yibai Li
,
Guangqiao Cao
,
Dong Liu
,
Zhang Jinlong
,
Liang Li
,
Cong Chen
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/3655804
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/3655804
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 (R2) 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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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/3655804
- https://downloads.hindawi.com/journals/cin/2022/3655804.pdf
- OA Status
- hybrid
- Cited By
- 6
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309905513
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- OpenAlex ID
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https://openalex.org/W4309905513Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2022/3655804Digital Object Identifier
- Title
-
Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-24Full publication date if available
- Authors
-
Yibai Li, Guangqiao Cao, Dong Liu, Zhang Jinlong, Liang Li, Cong ChenList of authors in order
- Landing page
-
https://doi.org/10.1155/2022/3655804Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/cin/2022/3655804.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://downloads.hindawi.com/journals/cin/2022/3655804.pdfDirect OA link when available
- Concepts
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Multispectral image, Convolutional neural network, Computer science, Decision tree, Artificial intelligence, Artificial neural network, Mean squared error, Pattern recognition (psychology), Support vector machine, Regression analysis, Remote sensing, Machine learning, Statistics, Mathematics, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 2Per-year citation counts (last 5 years)
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34Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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