Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index
Estimating the crop leaf area index (LAI) accurately is very critical in agricultural remote sensing, especially in monitoring crop growth and yield prediction. The development of unmanned aerial vehicles (UAVs) has been significant in recent years and has been extensively applied in agricultural re...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.957870/full |
| _version_ | 1811315915226087424 |
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| author | Cong Zhou Yan Gong Yan Gong Shenghui Fang Shenghui Fang Kaili Yang Yi Peng Yi Peng Xianting Wu Xianting Wu Renshan Zhu Renshan Zhu |
| author_facet | Cong Zhou Yan Gong Yan Gong Shenghui Fang Shenghui Fang Kaili Yang Yi Peng Yi Peng Xianting Wu Xianting Wu Renshan Zhu Renshan Zhu |
| author_sort | Cong Zhou |
| collection | DOAJ |
| description | Estimating the crop leaf area index (LAI) accurately is very critical in agricultural remote sensing, especially in monitoring crop growth and yield prediction. The development of unmanned aerial vehicles (UAVs) has been significant in recent years and has been extensively applied in agricultural remote sensing (RS). The vegetation index (VI), which reflects spectral information, is a commonly used RS method for estimating LAI. Texture features can reflect the differences in the canopy structure of rice at different growth stages. In this research, a method was developed to improve the accuracy of rice LAI estimation during the whole growing season by combining texture information based on wavelet transform and spectral information derived from the VI. During the whole growth period, we obtained UAV images of two study areas using a 12-band Mini-MCA system and performed corresponding ground measurements. Several VI values were calculated, and the texture analysis was carried out. New indices were constructed by mathematically combining the wavelet texture and spectral information. Compared with the corresponding VIs, the new indices reduced the saturation effect and were less sensitive to the emergence of panicles. The determination coefficient (R2) increased for most VIs used in this study throughout the whole growth period. The results indicated that the estimation accuracy of LAI by combining spectral information and texture information was higher than that of VIs. The method proposed in this study used the spectral and wavelet texture features extracted from UAV images to establish a model of the whole growth period of rice, which was easy to operate and had great potential for large-scale auxiliary rice breeding and field management research. |
| first_indexed | 2024-04-13T11:38:43Z |
| format | Article |
| id | doaj.art-9c8ca166a9024783862d92e200469d5c |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-13T11:38:43Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-9c8ca166a9024783862d92e200469d5c2022-12-22T02:48:21ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-08-011310.3389/fpls.2022.957870957870Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area indexCong Zhou0Yan Gong1Yan Gong2Shenghui Fang3Shenghui Fang4Kaili Yang5Yi Peng6Yi Peng7Xianting Wu8Xianting Wu9Renshan Zhu10Renshan Zhu11School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaLab for Remote Sensing of Crop Phenotyping, Wuhan University, Wuhan, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaLab for Remote Sensing of Crop Phenotyping, Wuhan University, Wuhan, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaLab for Remote Sensing of Crop Phenotyping, Wuhan University, Wuhan, ChinaLab for Remote Sensing of Crop Phenotyping, Wuhan University, Wuhan, ChinaCollege of Life Sciences, Wuhan University, Wuhan, ChinaLab for Remote Sensing of Crop Phenotyping, Wuhan University, Wuhan, ChinaCollege of Life Sciences, Wuhan University, Wuhan, ChinaEstimating the crop leaf area index (LAI) accurately is very critical in agricultural remote sensing, especially in monitoring crop growth and yield prediction. The development of unmanned aerial vehicles (UAVs) has been significant in recent years and has been extensively applied in agricultural remote sensing (RS). The vegetation index (VI), which reflects spectral information, is a commonly used RS method for estimating LAI. Texture features can reflect the differences in the canopy structure of rice at different growth stages. In this research, a method was developed to improve the accuracy of rice LAI estimation during the whole growing season by combining texture information based on wavelet transform and spectral information derived from the VI. During the whole growth period, we obtained UAV images of two study areas using a 12-band Mini-MCA system and performed corresponding ground measurements. Several VI values were calculated, and the texture analysis was carried out. New indices were constructed by mathematically combining the wavelet texture and spectral information. Compared with the corresponding VIs, the new indices reduced the saturation effect and were less sensitive to the emergence of panicles. The determination coefficient (R2) increased for most VIs used in this study throughout the whole growth period. The results indicated that the estimation accuracy of LAI by combining spectral information and texture information was higher than that of VIs. The method proposed in this study used the spectral and wavelet texture features extracted from UAV images to establish a model of the whole growth period of rice, which was easy to operate and had great potential for large-scale auxiliary rice breeding and field management research.https://www.frontiersin.org/articles/10.3389/fpls.2022.957870/fullleaf area index (LAI)vegetation index (VI)unmanned aerial vehicle (UAV)remote sensing (RS)texturewavelet |
| spellingShingle | Cong Zhou Yan Gong Yan Gong Shenghui Fang Shenghui Fang Kaili Yang Yi Peng Yi Peng Xianting Wu Xianting Wu Renshan Zhu Renshan Zhu Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index Frontiers in Plant Science leaf area index (LAI) vegetation index (VI) unmanned aerial vehicle (UAV) remote sensing (RS) texture wavelet |
| title | Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index |
| title_full | Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index |
| title_fullStr | Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index |
| title_full_unstemmed | Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index |
| title_short | Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index |
| title_sort | combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index |
| topic | leaf area index (LAI) vegetation index (VI) unmanned aerial vehicle (UAV) remote sensing (RS) texture wavelet |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.957870/full |
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