Application of non-linear partial least squares analysis on prediction of biomass of maize plants using hyperspectral images

With the application of hyperspectral imaging systems on high-throughput plant phenotyping, multivariable spectral data modelling methods have been proposed to predict plant physiological features such as biomass. However, most presented modelling methods are linear models such as the regression model between projected leaf area and plant biomass, which cannot accurately reflect some non-linear relationships between hyperspectral imaging information and the phenomes to be predicted. Therefore, it is important to develop a prediction model for plant physiological features that fully utilises the information in plant hyperspectral images. In this paper, a non-linear modelling method known as kernel partial least squares (KPLS) was investigated to improve the prediction performance of maize (US corn) biomass. The main idea of KPLS is mapping the original input features into higher dimensional feature spaces before obtaining the principle components for a standard PLS model. In the new mapping spaces, the original, nonlinear inputs may exhibit linear patterns with different kernel functions. This method was tested in a greenhouse assay containing 102 maize plants, which were subjected to a combination of three water treatments and two nitrogen treatments at three different growth stages. KPLS methods were successfully applied on the collected hyperspectral images. The results showed that compared with conventional linear models, statistics including R2 of KPLS with radial basis function (RBF) kernel had improved accuracy (R2 = 0.924). We expect that besides plant biomass, this new non-linear KPLS model could improve the quality of estimation of other plant features as well.