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1 Wu, S.; Deng, L.; Guo, L.; Wu, Y. 2022. Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery. Plant Methods, 18:68. [doi: https://doi.org/10.1186/s13007-022-00899-7]
Leaf area index ; Forecasting ; Unmanned aerial vehicles ; Thermal infrared imagery ; Data fusion ; Machine learning ; Estimation ; Wheat ; Vegetation index ; Remote sensing ; Satellites ; Biomass ; Models / China / Henan
(Location: IWMI HQ Call no: e-copy only Record No: H051401)
https://plantmethods.biomedcentral.com/counter/pdf/10.1186/s13007-022-00899-7.pdf
https://vlibrary.iwmi.org/pdf/H051401.pdf
https://vlibrary.iwmi.org/pdf/H051401.pdf
(7.53 MB) (7.53 MB)
Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.
Methods : To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.
Results: The results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R2 = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat.
Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.
Methods : To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.
Results: The results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R2 = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat.
Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.
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