Using fusion of texture features and vegetation indices from water concentration in rice crop to UAV remote sensing monitor
Water concentration is a key parameter to characterize crop physiological and healthy status. It is of great significance of employing unmanned aerial vehicle (UAV) low-altitude remote sensing technology to predict crop water concentration for crop breeding and precision agriculture management. UAV...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Editorial Office of Smart Agriculture
2020-03-01
|
| Series: | 智慧农业 |
| Subjects: | |
| Online Access: | http://www.smartag.net.cn/article/2020/2096-8094/2096-8094-2020-2-1-58.shtml |
| _version_ | 1818894381606764544 |
|---|---|
| author | Wan Liang Cen Haiyan Zhu Jiangpeng Zhang Jiafei Du Xiaoyue He Yong |
| author_facet | Wan Liang Cen Haiyan Zhu Jiangpeng Zhang Jiafei Du Xiaoyue He Yong |
| author_sort | Wan Liang |
| collection | DOAJ |
| description | Water concentration is a key parameter to characterize crop physiological and healthy status. It is of great significance of employing unmanned aerial vehicle (UAV) low-altitude remote sensing technology to predict crop water concentration for crop breeding and precision agriculture management. UAV remote sensing has been widely used for monitoring crop growth status, mainly focusing on using vegetation indices to estimate crop growth parameters at single or several growth stages. Few studies have been performed on evaluating crop water concentration. Consequently, this study mainly used vegetation indices and texture features extracted from UAV-based RGB and multispectral images to monitor water concentration of rice crop during the whole growth period. Firstly, a multi-rotor UAV equipped with high-resolution RGB and multispectral cameras to collect canopy images of rice crop, and water concentration was also measured by ground sampling. Then, vegetation indices and texture features calculated from RGB and multispectral images were used to analyze the growth changes of rice. Finally, random forest regression method was used to establish a prediction model of water concentration based on different image features. The results show that: (1) vegetation index, texture features and ground-measured water concentration could be used to dynamically monitor rice growth, and there existed correlations among these parameters; (2) image features extracted from multispectral images possessed more potential than those from RGB images to evaluate water concentration of rice crop, and normalized difference spectral index NDSI771, 611 achieved the best prediction accuracy (R2 = 0.68, RMSEP = 0.039, rRMSE = 5.24%); (3) fusing vegetation indices and texture features could further improve the prediction of water concentration (R2 = 0.86, RMSEP = 0.026, rRMSE = 3.21%), and the prediction error of RMSEP was reduced by 16.13% and 18.75%, respectively. These results demonstrats that it is feasible to apply UAV-based remote sensing to monitor water concentration of rice crop, which provides a new insight for precision irrigation and decision making of field management. |
| first_indexed | 2024-12-19T18:27:35Z |
| format | Article |
| id | doaj.art-7e9b356bef42495c8019d0325c5be037 |
| institution | Directory Open Access Journal |
| issn | 2096-8094 |
| language | English |
| last_indexed | 2024-12-19T18:27:35Z |
| publishDate | 2020-03-01 |
| publisher | Editorial Office of Smart Agriculture |
| record_format | Article |
| series | 智慧农业 |
| spelling | doaj.art-7e9b356bef42495c8019d0325c5be0372022-12-21T20:10:48ZengEditorial Office of Smart Agriculture智慧农业2096-80942020-03-0121586710.12133/j.smartag.2020.2.1.201911-SA002201911-SA002Using fusion of texture features and vegetation indices from water concentration in rice crop to UAV remote sensing monitorWan Liang0Cen Haiyan1Zhu Jiangpeng2Zhang Jiafei3Du Xiaoyue4He Yong5College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaWater concentration is a key parameter to characterize crop physiological and healthy status. It is of great significance of employing unmanned aerial vehicle (UAV) low-altitude remote sensing technology to predict crop water concentration for crop breeding and precision agriculture management. UAV remote sensing has been widely used for monitoring crop growth status, mainly focusing on using vegetation indices to estimate crop growth parameters at single or several growth stages. Few studies have been performed on evaluating crop water concentration. Consequently, this study mainly used vegetation indices and texture features extracted from UAV-based RGB and multispectral images to monitor water concentration of rice crop during the whole growth period. Firstly, a multi-rotor UAV equipped with high-resolution RGB and multispectral cameras to collect canopy images of rice crop, and water concentration was also measured by ground sampling. Then, vegetation indices and texture features calculated from RGB and multispectral images were used to analyze the growth changes of rice. Finally, random forest regression method was used to establish a prediction model of water concentration based on different image features. The results show that: (1) vegetation index, texture features and ground-measured water concentration could be used to dynamically monitor rice growth, and there existed correlations among these parameters; (2) image features extracted from multispectral images possessed more potential than those from RGB images to evaluate water concentration of rice crop, and normalized difference spectral index NDSI771, 611 achieved the best prediction accuracy (R2 = 0.68, RMSEP = 0.039, rRMSE = 5.24%); (3) fusing vegetation indices and texture features could further improve the prediction of water concentration (R2 = 0.86, RMSEP = 0.026, rRMSE = 3.21%), and the prediction error of RMSEP was reduced by 16.13% and 18.75%, respectively. These results demonstrats that it is feasible to apply UAV-based remote sensing to monitor water concentration of rice crop, which provides a new insight for precision irrigation and decision making of field management.http://www.smartag.net.cn/article/2020/2096-8094/2096-8094-2020-2-1-58.shtmlunmanned aerial vehicle (uav)water concentration of ricergb imagemultispectral imagevegetation indicestexture featurefeature fusion |
| spellingShingle | Wan Liang Cen Haiyan Zhu Jiangpeng Zhang Jiafei Du Xiaoyue He Yong Using fusion of texture features and vegetation indices from water concentration in rice crop to UAV remote sensing monitor 智慧农业 unmanned aerial vehicle (uav) water concentration of rice rgb image multispectral image vegetation indices texture feature feature fusion |
| title | Using fusion of texture features and vegetation indices from water concentration in rice crop to UAV remote sensing monitor |
| title_full | Using fusion of texture features and vegetation indices from water concentration in rice crop to UAV remote sensing monitor |
| title_fullStr | Using fusion of texture features and vegetation indices from water concentration in rice crop to UAV remote sensing monitor |
| title_full_unstemmed | Using fusion of texture features and vegetation indices from water concentration in rice crop to UAV remote sensing monitor |
| title_short | Using fusion of texture features and vegetation indices from water concentration in rice crop to UAV remote sensing monitor |
| title_sort | using fusion of texture features and vegetation indices from water concentration in rice crop to uav remote sensing monitor |
| topic | unmanned aerial vehicle (uav) water concentration of rice rgb image multispectral image vegetation indices texture feature feature fusion |
| url | http://www.smartag.net.cn/article/2020/2096-8094/2096-8094-2020-2-1-58.shtml |
| work_keys_str_mv | AT wanliang usingfusionoftexturefeaturesandvegetationindicesfromwaterconcentrationinricecroptouavremotesensingmonitor AT cenhaiyan usingfusionoftexturefeaturesandvegetationindicesfromwaterconcentrationinricecroptouavremotesensingmonitor AT zhujiangpeng usingfusionoftexturefeaturesandvegetationindicesfromwaterconcentrationinricecroptouavremotesensingmonitor AT zhangjiafei usingfusionoftexturefeaturesandvegetationindicesfromwaterconcentrationinricecroptouavremotesensingmonitor AT duxiaoyue usingfusionoftexturefeaturesandvegetationindicesfromwaterconcentrationinricecroptouavremotesensingmonitor AT heyong usingfusionoftexturefeaturesandvegetationindicesfromwaterconcentrationinricecroptouavremotesensingmonitor |