A Rice Leaf Area Index Monitoring Method Based on the Fusion of Data from RGB Camera and Multi-Spectral Camera on an Inspection Robot
Automated monitoring of the rice leaf area index (LAI) using near-ground sensing platforms, such as inspection robots, is essential for modern rice precision management. These robots are equipped with various complementary sensors, where specific sensor capabilities partially overlap to provide redu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4725 |
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| author | Yan Li Xuerui Qi Yucheng Cai Yongchao Tian Yan Zhu Weixing Cao Xiaohu Zhang |
| author_facet | Yan Li Xuerui Qi Yucheng Cai Yongchao Tian Yan Zhu Weixing Cao Xiaohu Zhang |
| author_sort | Yan Li |
| collection | DOAJ |
| description | Automated monitoring of the rice leaf area index (LAI) using near-ground sensing platforms, such as inspection robots, is essential for modern rice precision management. These robots are equipped with various complementary sensors, where specific sensor capabilities partially overlap to provide redundancy and enhanced reliability. Thus, leveraging multi-sensor fusion technology to improve the accuracy of LAI monitoring has become a crucial research focus. This study presents a rice LAI monitoring model based on the fused data from RGB and multi-spectral cameras with an ensemble learning algorithm. The results indicate that the estimation accuracy of the rice LAI monitoring model is effectively improved by fusing the vegetation index and textures from RGB and multi-spectral sensors. The model based on the LightGBM regression algorithm has the most improvement in accuracy, with a coefficient of determination (R<sup>2</sup>) of 0.892, a root mean square error (RMSE) of 0.270, and a mean absolute error (MAE) of 0.160. Furthermore, the accuracy of LAI estimation in the jointing stage is higher than in the heading stage. At the jointing stage, both LightGBM based on optimal RGB image features and Random Forest based on fused features achieved an R<sup>2</sup> of 0.95. This study provides a technical reference for automatically monitoring rice growth parameters in the field using inspection robots. |
| format | Article |
| id | doaj-art-baaedbd12aa741099da647a6cd69412d |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-baaedbd12aa741099da647a6cd69412d2025-08-20T02:01:29ZengMDPI AGRemote Sensing2072-42922024-12-011624472510.3390/rs16244725A Rice Leaf Area Index Monitoring Method Based on the Fusion of Data from RGB Camera and Multi-Spectral Camera on an Inspection RobotYan Li0Xuerui Qi1Yucheng Cai2Yongchao Tian3Yan Zhu4Weixing Cao5Xiaohu Zhang6National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, ChinaAutomated monitoring of the rice leaf area index (LAI) using near-ground sensing platforms, such as inspection robots, is essential for modern rice precision management. These robots are equipped with various complementary sensors, where specific sensor capabilities partially overlap to provide redundancy and enhanced reliability. Thus, leveraging multi-sensor fusion technology to improve the accuracy of LAI monitoring has become a crucial research focus. This study presents a rice LAI monitoring model based on the fused data from RGB and multi-spectral cameras with an ensemble learning algorithm. The results indicate that the estimation accuracy of the rice LAI monitoring model is effectively improved by fusing the vegetation index and textures from RGB and multi-spectral sensors. The model based on the LightGBM regression algorithm has the most improvement in accuracy, with a coefficient of determination (R<sup>2</sup>) of 0.892, a root mean square error (RMSE) of 0.270, and a mean absolute error (MAE) of 0.160. Furthermore, the accuracy of LAI estimation in the jointing stage is higher than in the heading stage. At the jointing stage, both LightGBM based on optimal RGB image features and Random Forest based on fused features achieved an R<sup>2</sup> of 0.95. This study provides a technical reference for automatically monitoring rice growth parameters in the field using inspection robots.https://www.mdpi.com/2072-4292/16/24/4725riceleaf area indexinspection robotsensor fusionensemble learning |
| spellingShingle | Yan Li Xuerui Qi Yucheng Cai Yongchao Tian Yan Zhu Weixing Cao Xiaohu Zhang A Rice Leaf Area Index Monitoring Method Based on the Fusion of Data from RGB Camera and Multi-Spectral Camera on an Inspection Robot Remote Sensing rice leaf area index inspection robot sensor fusion ensemble learning |
| title | A Rice Leaf Area Index Monitoring Method Based on the Fusion of Data from RGB Camera and Multi-Spectral Camera on an Inspection Robot |
| title_full | A Rice Leaf Area Index Monitoring Method Based on the Fusion of Data from RGB Camera and Multi-Spectral Camera on an Inspection Robot |
| title_fullStr | A Rice Leaf Area Index Monitoring Method Based on the Fusion of Data from RGB Camera and Multi-Spectral Camera on an Inspection Robot |
| title_full_unstemmed | A Rice Leaf Area Index Monitoring Method Based on the Fusion of Data from RGB Camera and Multi-Spectral Camera on an Inspection Robot |
| title_short | A Rice Leaf Area Index Monitoring Method Based on the Fusion of Data from RGB Camera and Multi-Spectral Camera on an Inspection Robot |
| title_sort | rice leaf area index monitoring method based on the fusion of data from rgb camera and multi spectral camera on an inspection robot |
| topic | rice leaf area index inspection robot sensor fusion ensemble learning |
| url | https://www.mdpi.com/2072-4292/16/24/4725 |
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