Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography
Abstract Background The technology of cotton defoliation is essential for mechanical cotton harvesting. Agricultural unmanned aerial vehicle (UAV) spraying has the advantages of low cost, high efficiency and no mechanical damage to cotton and has been favored and widely used by cotton planters in Ch...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-12-01
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| Series: | Plant Methods |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13007-022-00966-z |
| _version_ | 1811177866672472064 |
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| author | Jinyong Wu Sheng Wen Yubin Lan Xuanchun Yin Jiantao Zhang Yufeng Ge |
| author_facet | Jinyong Wu Sheng Wen Yubin Lan Xuanchun Yin Jiantao Zhang Yufeng Ge |
| author_sort | Jinyong Wu |
| collection | DOAJ |
| description | Abstract Background The technology of cotton defoliation is essential for mechanical cotton harvesting. Agricultural unmanned aerial vehicle (UAV) spraying has the advantages of low cost, high efficiency and no mechanical damage to cotton and has been favored and widely used by cotton planters in China. However, there are also some problems of low cotton defoliation rates and high impurity rates caused by unclear spraying amounts of cotton defoliants. The chemical rate recommendation and application should be based upon crop canopy volume rather than on land area. Plant height and leaf area index (LAI) is directly connected to plant canopy structure. Accurate dynamic monitoring of plant height and LAI provides important information for evaluating cotton growth and production. The traditional method to obtain plant height and LAI was s a time-consuming and labor-intensive task. It is very difficult and unrealistic to use the traditional measurement method to make the temporal and spatial variation map of plant height and LAI of large cotton fields. With the application of UAV in agriculture, remote sensing by UAV is currently regarded as an effective technology for monitoring and estimating plant height and LAI. Results In this paper, we used UAV RGB photos to build dense point clouds to estimate cotton plant height and LAI following cotton defoliant spraying. The results indicate that the proposed method was able to dynamically monitor the changes in the LAI of cotton at different times. At 3 days after defoliant spraying, the correlation between the plant height estimated based on the constructed dense point cloud and the measured plant height was strong, with $$R^2$$ R 2 and RMSE values of 0.962 and 0.913, respectively. At 10 days after defoliant spraying, the correlation became weaker over time, with $$R^2$$ R 2 and RMSE values of 0.018 and 0.027, respectively. Comparing the actual manually measured LAI with the estimated LAI based on the dense point cloud, the $$R^2$$ R 2 and RMSE were 0.872 and 0.814 and 0.132 and 0.173 at 3 and 10 days after defoliant spraying, respectively. Conclusions Dense point cloud construction based on UAV remote sensing is a potential alternative to plant height and LAI estimation. The accuracy of LAI estimation can be improved by considering both plant height and planting density. |
| first_indexed | 2024-04-11T06:09:35Z |
| format | Article |
| id | doaj.art-51b0da41d1964007bf1b805254868504 |
| institution | Directory Open Access Journal |
| issn | 1746-4811 |
| language | English |
| last_indexed | 2024-04-11T06:09:35Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj.art-51b0da41d1964007bf1b8052548685042022-12-22T04:41:20ZengBMCPlant Methods1746-48112022-12-0118111910.1186/s13007-022-00966-zEstimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photographyJinyong Wu0Sheng Wen1Yubin Lan2Xuanchun Yin3Jiantao Zhang4Yufeng Ge5Engineering College, South China Agricultural UniversityEngineering College, South China Agricultural UniversityNational Center for International Collaboration Research on Precision Agriculture Aviation Pesticides Praying Technology, South China Agricultural UniversityEngineering College, South China Agricultural UniversityNational Center for International Collaboration Research on Precision Agriculture Aviation Pesticides Praying Technology, South China Agricultural UniversityDepartment of Biological Systems Engineering, University of Nebraska-LincolnAbstract Background The technology of cotton defoliation is essential for mechanical cotton harvesting. Agricultural unmanned aerial vehicle (UAV) spraying has the advantages of low cost, high efficiency and no mechanical damage to cotton and has been favored and widely used by cotton planters in China. However, there are also some problems of low cotton defoliation rates and high impurity rates caused by unclear spraying amounts of cotton defoliants. The chemical rate recommendation and application should be based upon crop canopy volume rather than on land area. Plant height and leaf area index (LAI) is directly connected to plant canopy structure. Accurate dynamic monitoring of plant height and LAI provides important information for evaluating cotton growth and production. The traditional method to obtain plant height and LAI was s a time-consuming and labor-intensive task. It is very difficult and unrealistic to use the traditional measurement method to make the temporal and spatial variation map of plant height and LAI of large cotton fields. With the application of UAV in agriculture, remote sensing by UAV is currently regarded as an effective technology for monitoring and estimating plant height and LAI. Results In this paper, we used UAV RGB photos to build dense point clouds to estimate cotton plant height and LAI following cotton defoliant spraying. The results indicate that the proposed method was able to dynamically monitor the changes in the LAI of cotton at different times. At 3 days after defoliant spraying, the correlation between the plant height estimated based on the constructed dense point cloud and the measured plant height was strong, with $$R^2$$ R 2 and RMSE values of 0.962 and 0.913, respectively. At 10 days after defoliant spraying, the correlation became weaker over time, with $$R^2$$ R 2 and RMSE values of 0.018 and 0.027, respectively. Comparing the actual manually measured LAI with the estimated LAI based on the dense point cloud, the $$R^2$$ R 2 and RMSE were 0.872 and 0.814 and 0.132 and 0.173 at 3 and 10 days after defoliant spraying, respectively. Conclusions Dense point cloud construction based on UAV remote sensing is a potential alternative to plant height and LAI estimation. The accuracy of LAI estimation can be improved by considering both plant height and planting density.https://doi.org/10.1186/s13007-022-00966-zCrop heightLeaf area indexPlant phenotypingUAVStructure from motion |
| spellingShingle | Jinyong Wu Sheng Wen Yubin Lan Xuanchun Yin Jiantao Zhang Yufeng Ge Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography Plant Methods Crop height Leaf area index Plant phenotyping UAV Structure from motion |
| title | Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography |
| title_full | Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography |
| title_fullStr | Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography |
| title_full_unstemmed | Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography |
| title_short | Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography |
| title_sort | estimation of cotton canopy parameters based on unmanned aerial vehicle uav oblique photography |
| topic | Crop height Leaf area index Plant phenotyping UAV Structure from motion |
| url | https://doi.org/10.1186/s13007-022-00966-z |
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