UAV Remote Sensing Technology for Wheat Growth Monitoring in Precision Agriculture: Comparison of Data Quality and Growth Parameter Inversion
The quality of the image data and the potential to invert crop growth parameters are essential for effectively using unmanned aerial vehicle (UAV)-based sensor systems in precision agriculture (PA). However, the existing research falls short in providing a comprehensive examination of sensor data qu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/1/159 |
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| author | Jikai Liu Weiqiang Wang Jun Li Ghulam Mustafa Xiangxiang Su Ying Nian Qiang Ma Fengxian Zhen Wenhui Wang Xinwei Li |
| author_facet | Jikai Liu Weiqiang Wang Jun Li Ghulam Mustafa Xiangxiang Su Ying Nian Qiang Ma Fengxian Zhen Wenhui Wang Xinwei Li |
| author_sort | Jikai Liu |
| collection | DOAJ |
| description | The quality of the image data and the potential to invert crop growth parameters are essential for effectively using unmanned aerial vehicle (UAV)-based sensor systems in precision agriculture (PA). However, the existing research falls short in providing a comprehensive examination of sensor data quality and the inversion potential of crop growth parameters, and there is still ambiguity regarding how the quality of data affects the inversion potential. Therefore, this study explored the application potential of RGB and multispectral (MS) images acquired from three lightweight UAV platforms in the realm of PA: the DJI Mavic 2 Pro (M2P), Phantom 4 Multispectral (P4M), and Mavic 3 Multispectral (M3M). The reliability of pixel-scale data quality was evaluated based on image quality assessment metrics, and three winter wheat growth parameters, above-ground biomass (AGB), plant nitrogen content (PNC) and soil and plant analysis development (SPAD), were inverted using machine learning models based on multi-source image features at the plot scale. The results indicated that the RGB image quality from the M3M outperformed that of the M2P, while the MS image quality was marginally superior to that of the P4M. Nevertheless, these advantages in pixel-scale data quality did not improve inversion accuracy for crop parameters at the plot scale. Spectral features (SFs) derived from the P4M-based MS sensor demonstrated significant advantages in AGB inversion (R<sup>2</sup> = 0.86, rRMSE = 27.47%), while SFs derived from the M2P-based RGB camera exhibited the best performance in SPAD inversion (R<sup>2</sup> = 0.60, rRMSE = 7.67%). Additionally, combining spectral and textural features derived from the P4M-based MS sensor yielded the highest accuracy in PNC inversion (R<sup>2</sup> = 0.82, rRMSE = 14.62%). This study clarified the data quality of three prevalent UAV mounted sensor systems in PA and their influence on parameter inversion potential, offering guidance for selecting appropriate sensors and monitoring key crop growth parameters. |
| format | Article |
| id | doaj-art-d0dbb0873aef4f73b6444128dc3abeee |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-d0dbb0873aef4f73b6444128dc3abeee2025-01-24T13:16:58ZengMDPI AGAgronomy2073-43952025-01-0115115910.3390/agronomy15010159UAV Remote Sensing Technology for Wheat Growth Monitoring in Precision Agriculture: Comparison of Data Quality and Growth Parameter InversionJikai Liu0Weiqiang Wang1Jun Li2Ghulam Mustafa3Xiangxiang Su4Ying Nian5Qiang Ma6Fengxian Zhen7Wenhui Wang8Xinwei 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, ChinaKey Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 213200, 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 Agriculture, Anhui Science and Technology University, Fengyang 233100, ChinaCollege of Life Science, Langfang Normal University, Langfang 065000, ChinaCollege of Resource and Environment, Anhui Science and Technology University, Fengyang 233100, ChinaThe quality of the image data and the potential to invert crop growth parameters are essential for effectively using unmanned aerial vehicle (UAV)-based sensor systems in precision agriculture (PA). However, the existing research falls short in providing a comprehensive examination of sensor data quality and the inversion potential of crop growth parameters, and there is still ambiguity regarding how the quality of data affects the inversion potential. Therefore, this study explored the application potential of RGB and multispectral (MS) images acquired from three lightweight UAV platforms in the realm of PA: the DJI Mavic 2 Pro (M2P), Phantom 4 Multispectral (P4M), and Mavic 3 Multispectral (M3M). The reliability of pixel-scale data quality was evaluated based on image quality assessment metrics, and three winter wheat growth parameters, above-ground biomass (AGB), plant nitrogen content (PNC) and soil and plant analysis development (SPAD), were inverted using machine learning models based on multi-source image features at the plot scale. The results indicated that the RGB image quality from the M3M outperformed that of the M2P, while the MS image quality was marginally superior to that of the P4M. Nevertheless, these advantages in pixel-scale data quality did not improve inversion accuracy for crop parameters at the plot scale. Spectral features (SFs) derived from the P4M-based MS sensor demonstrated significant advantages in AGB inversion (R<sup>2</sup> = 0.86, rRMSE = 27.47%), while SFs derived from the M2P-based RGB camera exhibited the best performance in SPAD inversion (R<sup>2</sup> = 0.60, rRMSE = 7.67%). Additionally, combining spectral and textural features derived from the P4M-based MS sensor yielded the highest accuracy in PNC inversion (R<sup>2</sup> = 0.82, rRMSE = 14.62%). This study clarified the data quality of three prevalent UAV mounted sensor systems in PA and their influence on parameter inversion potential, offering guidance for selecting appropriate sensors and monitoring key crop growth parameters.https://www.mdpi.com/2073-4395/15/1/159image quality assessmentgrowth parameters inversionmachine learning modelsUAV-based sensorsprecision agriculture |
| spellingShingle | Jikai Liu Weiqiang Wang Jun Li Ghulam Mustafa Xiangxiang Su Ying Nian Qiang Ma Fengxian Zhen Wenhui Wang Xinwei Li UAV Remote Sensing Technology for Wheat Growth Monitoring in Precision Agriculture: Comparison of Data Quality and Growth Parameter Inversion Agronomy image quality assessment growth parameters inversion machine learning models UAV-based sensors precision agriculture |
| title | UAV Remote Sensing Technology for Wheat Growth Monitoring in Precision Agriculture: Comparison of Data Quality and Growth Parameter Inversion |
| title_full | UAV Remote Sensing Technology for Wheat Growth Monitoring in Precision Agriculture: Comparison of Data Quality and Growth Parameter Inversion |
| title_fullStr | UAV Remote Sensing Technology for Wheat Growth Monitoring in Precision Agriculture: Comparison of Data Quality and Growth Parameter Inversion |
| title_full_unstemmed | UAV Remote Sensing Technology for Wheat Growth Monitoring in Precision Agriculture: Comparison of Data Quality and Growth Parameter Inversion |
| title_short | UAV Remote Sensing Technology for Wheat Growth Monitoring in Precision Agriculture: Comparison of Data Quality and Growth Parameter Inversion |
| title_sort | uav remote sensing technology for wheat growth monitoring in precision agriculture comparison of data quality and growth parameter inversion |
| topic | image quality assessment growth parameters inversion machine learning models UAV-based sensors precision agriculture |
| url | https://www.mdpi.com/2073-4395/15/1/159 |
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