An Estimation of the Leaf Nitrogen Content of Apple Tree Canopies Based on Multispectral Unmanned Aerial Vehicle Imagery and Machine Learning Methods.
| Title: | An Estimation of the Leaf Nitrogen Content of Apple Tree Canopies Based on Multispectral Unmanned Aerial Vehicle Imagery and Machine Learning Methods. |
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| Authors: | Zhao, Xin1,2 (AUTHOR) 15730999979@163.com, Zhao, Zeyi1,2 (AUTHOR) zeyizhao@aliyun.com, Zhao, Fengnian1,2 (AUTHOR) zfn19990411@163.com, Liu, Jiangfan1,2 (AUTHOR) jiangfangliu@aliyun.com, Li, Zhaoyang1,2 (AUTHOR) lizhaoyang2i1@163.com, Wang, Xingpeng1,2,3 (AUTHOR) 13999068354@163.com, Gao, Yang3,4 (AUTHOR) 13999068354@163.com |
| Source: | Agronomy. Mar2024, Vol. 14 Issue 3, p552. 18p. |
| Subject Terms: | *FRUIT trees, *PARTIAL least squares regression, *MACHINE learning, *NITROGEN fertilizers, *ORCHARD management, *MULTISPECTRAL imaging |
| Geographic Terms: | XINJIANG Uygur Zizhiqu (China) |
| Abstract: | Accurate nitrogen fertilizer management determines the yield and quality of fruit trees, but there is a lack of multispectral UAV-based nitrogen fertilizer monitoring technology for orchards. Therefore, in this study, a field experiment was conducted by UAV to acquire multispectral images of an apple orchard with dwarf stocks and dense planting in southern Xinjiang and to estimate the nitrogen content of canopy leaves of apple trees by using three machine learning methods. The three inversion methods were partial least squares regression (PLSR), ridge regression (RR), and random forest regression (RFR). The results showed that the RF model could significantly improve the accuracy of estimating the leaf nitrogen content of the apple tree canopy, and the validation set of the four periods of apple trees ranged from 0.670 to 0.797 for R2, 0.838 mg L−1 to 4.403 mg L−1 for RMSE, and 1.74 to 2.222 for RPD, among which the RF model of the pre-fruit expansion stage of the 2023 season had the highest accuracy. This paper shows that the apple tree leaf nitrogen content estimation model based on multispectral UAV images constructed by using the RF machine learning method can timely and accurately diagnose the growth condition of apple trees, provide technical support for precise nitrogen fertilizer management in orchards, and provide a certain scientific basis for tree crop growth. [ABSTRACT FROM AUTHOR] |
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| Items | – Name: Title Label: Title Group: Ti Data: An Estimation of the Leaf Nitrogen Content of Apple Tree Canopies Based on Multispectral Unmanned Aerial Vehicle Imagery and Machine Learning Methods. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Xin%22">Zhao, Xin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> 15730999979@163.com</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Zeyi%22">Zhao, Zeyi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zeyizhao@aliyun.com</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Fengnian%22">Zhao, Fengnian</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zfn19990411@163.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Jiangfan%22">Liu, Jiangfan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jiangfangliu@aliyun.com</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Zhaoyang%22">Li, Zhaoyang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> lizhaoyang2i1@163.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Xingpeng%22">Wang, Xingpeng</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> 13999068354@163.com</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Yang%22">Gao, Yang</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<i> 13999068354@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Agronomy%22">Agronomy</searchLink>. Mar2024, Vol. 14 Issue 3, p552. 18p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22FRUIT+trees%22">FRUIT trees</searchLink><br />*<searchLink fieldCode="DE" term="%22PARTIAL+least+squares+regression%22">PARTIAL least squares regression</searchLink><br />*<searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br />*<searchLink fieldCode="DE" term="%22NITROGEN+fertilizers%22">NITROGEN fertilizers</searchLink><br />*<searchLink fieldCode="DE" term="%22ORCHARD+management%22">ORCHARD management</searchLink><br />*<searchLink fieldCode="DE" term="%22MULTISPECTRAL+imaging%22">MULTISPECTRAL imaging</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22XINJIANG+Uygur+Zizhiqu+%28China%29%22">XINJIANG Uygur Zizhiqu (China)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate nitrogen fertilizer management determines the yield and quality of fruit trees, but there is a lack of multispectral UAV-based nitrogen fertilizer monitoring technology for orchards. Therefore, in this study, a field experiment was conducted by UAV to acquire multispectral images of an apple orchard with dwarf stocks and dense planting in southern Xinjiang and to estimate the nitrogen content of canopy leaves of apple trees by using three machine learning methods. The three inversion methods were partial least squares regression (PLSR), ridge regression (RR), and random forest regression (RFR). The results showed that the RF model could significantly improve the accuracy of estimating the leaf nitrogen content of the apple tree canopy, and the validation set of the four periods of apple trees ranged from 0.670 to 0.797 for R2, 0.838 mg L−1 to 4.403 mg L−1 for RMSE, and 1.74 to 2.222 for RPD, among which the RF model of the pre-fruit expansion stage of the 2023 season had the highest accuracy. This paper shows that the apple tree leaf nitrogen content estimation model based on multispectral UAV images constructed by using the RF machine learning method can timely and accurately diagnose the growth condition of apple trees, provide technical support for precise nitrogen fertilizer management in orchards, and provide a certain scientific basis for tree crop growth. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Agronomy is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/agronomy14030552 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 552 Subjects: – SubjectFull: XINJIANG Uygur Zizhiqu (China) Type: general – SubjectFull: FRUIT trees Type: general – SubjectFull: PARTIAL least squares regression Type: general – SubjectFull: MACHINE learning Type: general – SubjectFull: NITROGEN fertilizers Type: general – SubjectFull: ORCHARD management Type: general – SubjectFull: MULTISPECTRAL imaging Type: general Titles: – TitleFull: An Estimation of the Leaf Nitrogen Content of Apple Tree Canopies Based on Multispectral Unmanned Aerial Vehicle Imagery and Machine Learning Methods. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhao, Xin – PersonEntity: Name: NameFull: Zhao, Zeyi – PersonEntity: Name: NameFull: Zhao, Fengnian – PersonEntity: Name: NameFull: Liu, Jiangfan – PersonEntity: Name: NameFull: Li, Zhaoyang – PersonEntity: Name: NameFull: Wang, Xingpeng – PersonEntity: Name: NameFull: Gao, Yang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 20734395 Numbering: – Type: volume Value: 14 – Type: issue Value: 3 Titles: – TitleFull: Agronomy Type: main |
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