Review of Research and Application for Vegetation BRDF.

Plant leaves, as a vital organ of the plant photosynthesis, directly reflects the plant growth and nutrition status. Internal physicochemical property model, based on the reflection and transmission of plant leaf, indirectly reflects the information of matter and energy exchange in the process of plant growth information, which can be the premise and foundation of the plant growth process of fine management. The optical properties of plant leaves plays a great significant role in the field of crop nutrition diagnosis and plant morphological structure based on remote sensing, in the field of virtual plant light transmission simulation and computer graphics et al.

[Method of Background Elimination for Wheat Leaves Based on the BPLT Model].

In order to precisely acquire leaf reflectance spectra (400-1 000 nm), influence of background on leaf reflectance spectra was studied. Experiment was conducted to discriminate the characteristics of wheat leaves based on 8 background materials and leaf chlorophyll concentration. BPLT (Background Plate) model, based on the Plate model, was promoted and applied to remove the influence of leave background.

[Study of detection of SPAD value in tomato leaves stressed by grey mold based on hyperspectral technique].

Hyperspectral imaging feature of chlorophyll content (SPAD) in tomato leaves stressed by grey mold was studied in the present paper. Hyperspectral imagings of healthy and infected tomato leaves were obtained by hyperspectral imaging system from 380 to 1 030 nm and diffuse spectral response of region of interest (ROI) from hyperspectral imaging was extracted by EN-VI software, then different preprocessing methods were used including smoothing and normalization etc. The partial least squares regress (PLSR) and principal component regress (PCR) models were developed for the prediction of SPAD value in tomato leaves based on normalization preprocessing method, then the back-propagation neural network (BPNN) and least squares-support vector machine (LS-SVM) models were built based on the four variables suggested by PLSR model.