Research on Monitoring Topping Time of Cotton Based on AdaBoost+Decision Tree
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, th...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/4214332 |
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| _version_ | 1832552085105672192 |
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| author | Yibai Li Guangqiao Cao Chao Ji Dong Liu Jinlong Zhang Liang Li Cong Chen |
| author_facet | Yibai Li Guangqiao Cao Chao Ji Dong Liu Jinlong Zhang Liang Li Cong Chen |
| author_sort | Yibai Li |
| collection | DOAJ |
| description | 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 (R2) between multispectrum and cotton plant height is 0.96, and the average prediction error (RMSEP) is 0.40 cm, the coefficient of determination (R2) between multispectrum information and the fruiting branches is 0.97, the prediction mean error (RMSEP) is 0.54, and the correlation determination (R2) 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. |
| format | Article |
| id | doaj-art-75d3a0b556ae4e6791a78da089d2f096 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-75d3a0b556ae4e6791a78da089d2f0962025-02-03T05:59:39ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/4214332Research on Monitoring Topping Time of Cotton Based on AdaBoost+Decision TreeYibai Li0Guangqiao Cao1Chao Ji2Dong Liu3Jinlong Zhang4Liang Li5Cong Chen6Nanjing Institute of Agricultural MechanizationNanjing Institute of Agricultural MechanizationInstitute of Mechanical EquipmentNanjing Institute of Agricultural MechanizationNanjing Institute of Agricultural MechanizationNanjing Institute of Agricultural MechanizationNanjing Institute of Agricultural MechanizationTopping 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 (R2) between multispectrum and cotton plant height is 0.96, and the average prediction error (RMSEP) is 0.40 cm, the coefficient of determination (R2) between multispectrum information and the fruiting branches is 0.97, the prediction mean error (RMSEP) is 0.54, and the correlation determination (R2) 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.http://dx.doi.org/10.1155/2022/4214332 |
| spellingShingle | Yibai Li Guangqiao Cao Chao Ji Dong Liu Jinlong Zhang Liang Li Cong Chen Research on Monitoring Topping Time of Cotton Based on AdaBoost+Decision Tree Discrete Dynamics in Nature and Society |
| title | Research on Monitoring Topping Time of Cotton Based on AdaBoost+Decision Tree |
| title_full | Research on Monitoring Topping Time of Cotton Based on AdaBoost+Decision Tree |
| title_fullStr | Research on Monitoring Topping Time of Cotton Based on AdaBoost+Decision Tree |
| title_full_unstemmed | Research on Monitoring Topping Time of Cotton Based on AdaBoost+Decision Tree |
| title_short | Research on Monitoring Topping Time of Cotton Based on AdaBoost+Decision Tree |
| title_sort | research on monitoring topping time of cotton based on adaboost decision tree |
| url | http://dx.doi.org/10.1155/2022/4214332 |
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