Research on Monitoring Topping Time of Cotton Based on AdaBoost+Decision Tree Article Swipe
Yibai Li
,
Guangqiao Cao
,
Chao Ji
,
Dong Liu
,
Jinlong Zhang
,
Liang Li
,
Cong Chen
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/4214332
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/4214332
Topping is an important part in cotton field management, the spraying time has a great impact on cotton quality. In agricultural production, the strategy of timing the cotton topping mainly relies on manual inspections and experience, which is lack of efficiency and science. To solve the problem, this paper uses a drone equipped with a multispectral camera to collect the multispectral information of the cotton canopy of 12 days which includes before and after the topping operation in Shihezi. At the same time, the information of cotton plant height, the number of fruiting branches, and flower buds are collected. Compare multiple band combinations and vegetation index; the combined data of 550 + 730 + 790 nm band is selected as the model input. AdaBoost + decision tree method is proposed as a fitting model, the fitting results show that the coefficient of determination ( R 2 ) between multispectrum and cotton plant height is 0.96, and the average prediction error (RMSEP) is 0.40 cm, the coefficient of determination ( R 2 ) between multispectrum information and the fruiting branches is 0.97, the prediction mean error (RMSEP) is 0.54, and the correlation determination ( R 2 ) with the flower buds is 0.84, and the prediction mean error is (RMSEP) 0.49. The output data of the fitting model is used as the input of the topping time discriminant model, and the discriminant model can obtain an accuracy of 94.03%. The method in this paper can effectively monitor the growth status of cotton in the topping time and provide a technical path to scientifically determine the cotton topping time.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/4214332
- https://downloads.hindawi.com/journals/ddns/2022/4214332.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4220702660
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4220702660Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2022/4214332Digital Object Identifier
- Title
-
Research on Monitoring Topping Time of Cotton Based on AdaBoost+Decision TreeWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Yibai Li, Guangqiao Cao, Chao Ji, Dong Liu, Jinlong Zhang, Liang Li, Cong ChenList of authors in order
- Landing page
-
https://doi.org/10.1155/2022/4214332Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/ddns/2022/4214332.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://downloads.hindawi.com/journals/ddns/2022/4214332.pdfDirect OA link when available
- Concepts
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Topping, Statistics, Discriminant, Computer science, Decision tree, Mathematics, Multispectral image, Linear discriminant analysis, Agricultural engineering, Artificial intelligence, Engineering, Horticulture, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- References (count)
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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