[Prediction Model on Net Photosynthetic Rate of Soybean Plant Groups Based on Kernel Function and Visible Light Spectrum].

The paper uses MSR-16 portable multispectral radiometer made in the USA and computes the numbers of the test units by pulling the formula on the radiometer effective observation area, which solves the problem on the uncertain numbers of computing the times on region visible light band spectral radiation ratio M_D. The paper uses CI-310 portable photosynthesis measurement system made by American CID Company and measures the net photosynthetic rate of a group of soybean plant. M_D and C_D are normalized by the normalization method [0,1].

[Prediction of Greenhouse Cucumber Disease Based on Chlorophyll Fluorescence Spectrum Index].

The occurrence of greenhouse vegetable diseases and its epidemic seriously affect the production and management of facility agriculture, which greatly reduce the economic benefits of facility agriculture. In order to achieve nondestructive and accurate prediction of greenhouse vegetable diseases, this paper taking cucumber downy mildew disease as the research object, constructed spectrum characteristic index by using chlorophyll fluorescence induced by laser and established the prediction model of greenhouse vegetable diseases. In this paper, the experiment used comparative analysis method.

[Rice blast prediction model based on analysis of chlorophyll fluorescence spectrum].

In order to detect rice blast more rapidly, accurately and nondestructively, the identification and early warning models of rice blast were established in the present research. First of all, rice blast was divided into three grades according to the relative area of disease spots in rice leaf and laser induced chlorophyll fluorescence spectra of rice leaves at different disease levels were measured in the paddy fields. Meanwhile, 502-830 nm bands of laser-induced chlorophyll fluorescence spectra were selected for the study of rice blast.

[Fluorescence spectrum monitor for early warning of greenhouse cucumber aphis pests].

The infection and degree of cucumber aphis pests was studied by analyzing chlorophyllfluorescence spectrum in greenhouse. Based on the configuration of the spectrum, characteristic points were established, in which the intensity of waveband F632 was the first characteristic point between healthy and aphis pests leaves. The second characteristic point was K which was the change rate of spectral curve from waveband F512 to F632.

[Chlorophyll fluorescence spectrum analysis of greenhouse cucumber disease and insect damage].

The present paper is based on chlorophyll fluorescence spectrum analysis. The wavelength 685 nm was determined as the primary characteristic point for the analysis of healthy or disease and insect damaged leaf by spectrum configuration. Dimensionality reduction of the spectrum was achieved by combining simple intercorrelation bands selection and principal component analysis (PCA).

[Cucumber downy mildew prediction model based on analysis of chlorophyll fluorescence spectrum].

In order to achieve quick and nondestructive prediction of cucumber disease, a prediction model of greenhouse cucumber downy mildew has been established and it is based on analysis technology of laser-induced chlorophyll fluorescence spectrum. By assaying the spectrum curve of healthy leaves, leaves inoculated with bacteria for three days and six days and after feature information extraction of those three groups of spectrum data using first-order derivative spectrum preprocessing with principal components and data reduction, principal components score scatter diagram has been built, and according to accumulation contribution rate, ten principal components have been selected to replace derivative spectrum curve, and then classification and prediction has been done by support vector machine. According to the training of 105 samples from the three groups, classification and prediction of 44 samples and comparing the classification capacities of four kernel function support vector machines, the consequence is that RBF has high quality in classification and identification and the accuracy rate in classification and prediction of cucumber downy mildew reaches 97.

[Prediction model of net photosynthetic rate of ginseng under forest based on optimized parameters support vector machine].

Using K-fold cross validation method and two support vector machine functions, four kernel functions, grid-search, genetic algorithm and particle swarm optimization, the authors constructed the support vector machine model of the best penalty parameter c and the best correlation coefficient. Using information granulation technology, the authors constructed P particle and epsilon particle about those factors affecting net photosynthetic rate, and reduced these dimensions of the determinant. P particle includes the percent of visible spectrum ingredients.

[Diagnosis of cucumber diseases and insect pests by fluorescence spectroscopy technology based on PCA-sVM].

The diagnosis model of the cucumber diseases and insect pests was established by laser-induced chlorophyll fluorescence (LICF) spectroscopy technology combined with support vector machines (SVM) algorithm in the present research. This model would be used to realize the fast and exact diagnosis of the cucumber diseases and insect pests. The noise of original spectrum was reduced by three methods, including Savitzky-Golay smoothing (SG), Savitzky-Golay smoothing combined with fast Fourier transform (FFT) and Savitzy-Golay smoothing combined with first derivative transform (FDT).