Estimation of Wheat Plant Height and Biomass by Combining UAV Imagery and Elevation Data

Dunliang Wang, Rui Li, Bo Zhu, Tao Liu, Chengming Sun () and Wenshan Guo
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Dunliang Wang: Jiangsu Key Laboratory of Crop Genetics and Physiology, Yangzhou 225009, China
Rui Li: Jiangsu Key Laboratory of Crop Genetics and Physiology, Yangzhou 225009, China
Bo Zhu: Jiangsu Key Laboratory of Crop Genetics and Physiology, Yangzhou 225009, China
Tao Liu: Jiangsu Key Laboratory of Crop Genetics and Physiology, Yangzhou 225009, China
Chengming Sun: Jiangsu Key Laboratory of Crop Genetics and Physiology, Yangzhou 225009, China
Wenshan Guo: Jiangsu Key Laboratory of Crop Genetics and Physiology, Yangzhou 225009, China

Agriculture, 2022, vol. 13, issue 1, 1-21

Abstract: Aboveground biomass (AGB) is an important basis for wheat yield formation. It is useful to timely collect the AGB data to monitor wheat growth and to build high-yielding wheat groups. However, as traditional AGB data acquisition relies on destructive sampling, it is difficult to adapt to the modernization of agriculture, and the estimation accuracy of spectral data alone is low and cannot solve the problem of index saturation at later stages. In this study, an unmanned aerial vehicle (UAV) with an RGB camera and the real-time kinematic (RTK) was used to obtain imagery data and elevation data at the same time during the critical fertility period of wheat. The cumulative percentile and the mean value methods were then used to extract the wheat plant height (PH), and the color indices (CIS) and PH were combined to invert the AGB of wheat using parametric and non-parametric models. The results showed that the accuracy of the model improved with the addition of elevation data, and the model with the highest accuracy of multi-fertility period estimation was PLSR (PH + CIS), with R 2 , RMSE and NRMSE of 0.81, 1248.48 kg/ha and 21.77%, respectively. Compared to the parametric models, the non-parametric models incorporating PH and CIS greatly improved the prediction of AGB during critical fertility periods in wheat. The inclusion of elevation data therefore greatly improves the accuracy of AGB prediction in wheat compared to traditional spectral prediction models. The fusion of UAV-based elevation data and image information provides a new technical tool for multi-season wheat AGB monitoring.

Keywords: wheat; UAV RGB image; color indices; AGB; elevation; regression models (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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