AUTHOR=Yao Xia , Si Haiyang , Cheng Tao , Jia Min , Chen Qi , Tian YongChao , Zhu Yan , Cao Weixing , Chen Chaoyan , Cai Jiayu , Gao Rongrong 

TITLE=Hyperspectral Estimation of Canopy Leaf Biomass Phenotype per Ground Area Using a Continuous Wavelet Analysis in Wheat

JOURNAL=Frontiers in Plant Science

VOLUME=9

YEAR=2018

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2018.01360

DOI=10.3389/fpls.2018.01360

ISSN=1664-462X

ABSTRACT=<p>To extend agricultural productivity by knowledge-based breeding and tailoring varieties to adapt to specific environmental conditions, it is imperative to improve our ability to acquire the dynamic changes of the crop’s phenotype under field conditions. Canopy leaf biomass (CLB) per ground area is one of the key crop phenotypic parameters in plant breeding. The most promising technique for effectively monitoring CLB is the hyperspectral vegetation index (VI). However, VI-based empirical models are limited by their poor stability and extrapolation difficulties when used to assess complex dynamic environments with different varieties, growth stages, and sites. It has been proven difficult to calibrate and validate some VI-based models. To address this problem, eight field experiments using eight wheat varieties were conducted during the period of 2003–2011 at four sites, and continuous wavelet transform (CWT) was applied to estimate CLB from large number of field experimental data. The analysis of 108 wavelet functions from all 15 wavelet families revealed that the best wavelet features for CLB in terms of wavelength (W) and scale (S) were observed in the near-infrared region and at high scales (7 and 8). The best wavelet-based model was derived from the Daubechies family (db), and was named db7 (W<sub>1197</sub> nm, S<sub>8</sub>). The new model was more accurate (<inline-formula><mml:math id="M1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msubsup><mml:mrow><mml:mi mathvariant="italic">R</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">v</mml:mi></mml:mrow><mml:mrow><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:math></inline-formula> = 0.67 and RRMSE = 27.26%) than a model obtained using the best existing VI (<inline-formula><mml:math id="M2" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msubsup><mml:mrow><mml:mi mathvariant="italic">R</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">v</mml:mi></mml:mrow><mml:mrow><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:math></inline-formula> = 0.54 and RRMSE = 34.71%). Furthermore, the stable performance of the optimal db7 wavelet feature was confirmed by its limited variation among the different varieties, growth stages, and sites, which confirmed the high stability of the CWT to estimate CLB with hyperspectral data. This study highlighted the potential of precision phenotyping to assess the dynamic genetics of complex traits, especially those not amenable to traditional phenotyping.</p>