Jin Xiaoli, Shi Chunhai, Yu Chang Yeon, Yamada Toshihiko, Sacks Erik J
Department of Agronomy and the Key Laboratory of Crop Germplasm Resource of Zhejiang Province, Zhejiang UniversityHangzhou, China.
Division of Bioresource Sciences, Kangwon National UniversityChuncheon, South Korea.
Front Plant Sci. 2017 May 19;8:721. doi: 10.3389/fpls.2017.00721. eCollection 2017.
Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including . Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in . Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than the PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in . The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in , and thus very helpful for development of drought-resistant varieties in .
叶片含水量是限制包括……在内的植物光合作用效率和生物量生产力的最常见生理参数之一。因此，快速且无损地测定或预测含水量具有重要意义。在本研究中，我们探究了……中叶片含水量与漫反射光谱之间的关系。针对叶片含水量测定模型，开发了包括偏最小二乘法（PLS）、最小二乘支持向量机回归（LSSVR）和径向基函数（RBF）神经网络（NN）在内的三种多元校准方法。包括RBF_LSSVR和RBF_NN在内的非线性模型比PLS和线性LSSVR模型具有更高的准确性。此外，在……中确定了75个与叶片含水量密切相关的敏感波长。基于75个特征波长预测叶片含水量的RBF_LSSVR和RBF_NN模型分别获得了0.9838和0.9899的高决定系数。结果表明，使用这两个波长区间时，非线性模型比线性模型更准确。这些结果表明，可见/近红外（VIS/NIR）光谱结合RBF_LSSVR或RBF_NN是一种用于测定……中叶片含水量的有用的无损工具，因此对……中抗旱品种的开发非常有帮助。
