Rice Yield Estimation Based on Cumulative Time Series Vegetation Indices of UAV MS and RGB Images
Timely and accurate yield estimation is essential for effective crop management and the grain trade. Remote sensing has emerged as a valuable tool for monitoring rice yields; however, many studies concentrate on a single period or simply aggregate multiple periods, neglecting the complexities underl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/14/12/2956 |
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| author | Jun Li Weiqiang Wang Yali Sheng Sumera Anwar Xiangxiang Su Ying Nian Hu Yue Qiang Ma Jikai Liu Xinwei Li |
| author_facet | Jun Li Weiqiang Wang Yali Sheng Sumera Anwar Xiangxiang Su Ying Nian Hu Yue Qiang Ma Jikai Liu Xinwei Li |
| author_sort | Jun Li |
| collection | DOAJ |
| description | Timely and accurate yield estimation is essential for effective crop management and the grain trade. Remote sensing has emerged as a valuable tool for monitoring rice yields; however, many studies concentrate on a single period or simply aggregate multiple periods, neglecting the complexities underlying yield formation. The study enhances yield estimation by integrating cumulative time series vegetation indices (VIs) from multispectral (MS) and RGB (Red, Green, Blue) sensors to identify optimal combinations of growth periods. We utilized two unmanned aerial vehicle to capture spectral information from rice canopies through MS and RGB sensors. By analyzing the correlations between vegetation indices from different sensors and rice yields, the optimal MS-VIs and RGB-VIs for each period were identified. Following this, the relationship between the cumulative time series of MS-VIs, RGB-VIs, and rice yields was further examined. The results demonstrate that the booting stage is a crucial growth period influencing rice yield, with VIs exhibiting increased correlation with yield, peaking during this stage before declining. For the MS sensor, the rice yield model, based on the cumulative time series of MS-VIs from the tillering stage to the panicle initiation stage, achieves optimal accuracy (R<sup>2</sup> = 0.722, RRMSE = 0.555). For the RGB sensor, the rice yield model, based on the cumulative time series of RGB-VIs from the tillering stage to the grain-filling stage, yields the highest accuracy (R<sup>2</sup> = 0.727, RRMSE = 0.526). In comparison, the multi-sensor rice yield model, which combines the cumulative time series of MS-VIs from the tillering stage and RGB-VIs from the panicle initiation to grain-filling stages, achieves the highest accuracy with R<sup>2</sup> = 0.759 and RRMSE = 0.513. These findings suggest that cumulative time series VIs and the integration of multiple sensors enhance yield prediction accuracy, providing a comprehensive approach for estimating rice yield dynamics and supporting precision agriculture and informed crop management. |
| format | Article |
| id | doaj-art-6dec1c6fa7034bcc9f7040b9aef72654 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-6dec1c6fa7034bcc9f7040b9aef726542024-12-27T14:04:31ZengMDPI AGAgronomy2073-43952024-12-011412295610.3390/agronomy14122956Rice Yield Estimation Based on Cumulative Time Series Vegetation Indices of UAV MS and RGB ImagesJun Li0Weiqiang Wang1Yali Sheng2Sumera Anwar3Xiangxiang Su4Ying Nian5Hu Yue6Qiang Ma7Jikai Liu8Xinwei Li9College of Resource and Environment, Anhui Science and Technology University, Fengyang 233100, ChinaCollege of Resource and Environment, Anhui Science and Technology University, Fengyang 233100, ChinaCollege of Resource and Environment, Anhui Science and Technology University, Fengyang 233100, ChinaDepartment of Botany, Government College for Women University Faisalabad, Faisalabad 38000, PakistanCollege of Resource and Environment, Anhui Science and Technology University, Fengyang 233100, ChinaCollege of Resource and Environment, Anhui Science and Technology University, Fengyang 233100, ChinaCollege of Resource and Environment, Anhui Science and Technology University, Fengyang 233100, ChinaCollege of Resource and Environment, Anhui Science and Technology University, Fengyang 233100, ChinaCollege of Resource and Environment, Anhui Science and Technology University, Fengyang 233100, ChinaCollege of Resource and Environment, Anhui Science and Technology University, Fengyang 233100, ChinaTimely and accurate yield estimation is essential for effective crop management and the grain trade. Remote sensing has emerged as a valuable tool for monitoring rice yields; however, many studies concentrate on a single period or simply aggregate multiple periods, neglecting the complexities underlying yield formation. The study enhances yield estimation by integrating cumulative time series vegetation indices (VIs) from multispectral (MS) and RGB (Red, Green, Blue) sensors to identify optimal combinations of growth periods. We utilized two unmanned aerial vehicle to capture spectral information from rice canopies through MS and RGB sensors. By analyzing the correlations between vegetation indices from different sensors and rice yields, the optimal MS-VIs and RGB-VIs for each period were identified. Following this, the relationship between the cumulative time series of MS-VIs, RGB-VIs, and rice yields was further examined. The results demonstrate that the booting stage is a crucial growth period influencing rice yield, with VIs exhibiting increased correlation with yield, peaking during this stage before declining. For the MS sensor, the rice yield model, based on the cumulative time series of MS-VIs from the tillering stage to the panicle initiation stage, achieves optimal accuracy (R<sup>2</sup> = 0.722, RRMSE = 0.555). For the RGB sensor, the rice yield model, based on the cumulative time series of RGB-VIs from the tillering stage to the grain-filling stage, yields the highest accuracy (R<sup>2</sup> = 0.727, RRMSE = 0.526). In comparison, the multi-sensor rice yield model, which combines the cumulative time series of MS-VIs from the tillering stage and RGB-VIs from the panicle initiation to grain-filling stages, achieves the highest accuracy with R<sup>2</sup> = 0.759 and RRMSE = 0.513. These findings suggest that cumulative time series VIs and the integration of multiple sensors enhance yield prediction accuracy, providing a comprehensive approach for estimating rice yield dynamics and supporting precision agriculture and informed crop management.https://www.mdpi.com/2073-4395/14/12/2956remote sensingUAVyieldricegrowth stagesvegetation indices |
| spellingShingle | Jun Li Weiqiang Wang Yali Sheng Sumera Anwar Xiangxiang Su Ying Nian Hu Yue Qiang Ma Jikai Liu Xinwei Li Rice Yield Estimation Based on Cumulative Time Series Vegetation Indices of UAV MS and RGB Images Agronomy remote sensing UAV yield rice growth stages vegetation indices |
| title | Rice Yield Estimation Based on Cumulative Time Series Vegetation Indices of UAV MS and RGB Images |
| title_full | Rice Yield Estimation Based on Cumulative Time Series Vegetation Indices of UAV MS and RGB Images |
| title_fullStr | Rice Yield Estimation Based on Cumulative Time Series Vegetation Indices of UAV MS and RGB Images |
| title_full_unstemmed | Rice Yield Estimation Based on Cumulative Time Series Vegetation Indices of UAV MS and RGB Images |
| title_short | Rice Yield Estimation Based on Cumulative Time Series Vegetation Indices of UAV MS and RGB Images |
| title_sort | rice yield estimation based on cumulative time series vegetation indices of uav ms and rgb images |
| topic | remote sensing UAV yield rice growth stages vegetation indices |
| url | https://www.mdpi.com/2073-4395/14/12/2956 |
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