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

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]. Then, the normalization data M_D1 and C_D1 are gained . Based on the different test time, M_D1 is divided of M_D11 and M_D12. C_D1 is divided of C_D11 and C_D12. The paper uses polynomial kernel function, gauss kernel function, sigmoid kernel function and bio-selfadaption kernel function constructed by us with Support Vector Machine. Penalty parameter c and parameter g separately are optimized with optimization algorithms such as grid-search，genetic algorithm and particle swarm optimization. Based on the formula epsilon-SVR and the formula nu-SVR with Support Vector Machine, the paper constructs the prediction model on the net photosynthetic rate of a group of soybean plant by using of the cross combination with four kernel functions, three optimization methods and two formulas. The test results are as follows: in the condition of S=17 m2 which is the test plan area of soybean plant and the H=2 m which is the high on MSR-16 portable multispectral radiometer above the canopy of soybean plant, the prediction accuracy is up to 85% on the No.1 prediction set C_D12 and the prediction accuracy is up to 82% on the No.2 prediction set C_D12 based on the model epsilon-SVR-bio-selfadaption-grid-search. In the condition of other combinations with S and H, the prediction accuracy is up to 81% on the No.2 prediction set C_D12 based on the model epsilon-SVR-bio-selfadaption-grid-search. The model epsilon-SVR-bio-selfadaption-grid-search indicates the validity of bio-selfadaption kernel functions which is constructed by our previous research with support vector machine. The model epsilon-SVR-bio-selfadaption-grid-search indicates the rationality of the measure method on visible spectral data in the test area. The model epsilon-SVR-bio-selfadaption-grid-search indicates the feasibility of the prediction method on net photosynthetic rate of soybean plant groups by using of visible spectrum.