Prediction of nitrogen content rate of paddy rice leaf based on GA-LS-SVM

A prediction model of paddy rice leaf nitrogen content rate was built based on canopy spectrum reflectivity. The paddy rice of every nitrogen content was cultivated by man-made control, At a certain vegetation period, the paddy rice canopy spectrum reflectivity was gathered and the leaf nitrogen content rate was measured at the same time. Each canopy spectrum image was analyzed, and the characteristic wave band was chosen which matches the high relativity coefficient. Because the LS-SVM's parameters were difficult to be confirmed, genetic algorithm was adopted to carry out an optimization on LS-SVM parameter and the GA-LS-SVM algorithm was composed. Examination results indicate that, the traditional LS-SVM model's average return-judge accuracy reaches 97.21%, and it's average error ratio of prediction reaches 5.70%. The GA-LS-SVM model's average return-judge accuracy reaches 99.60%, and it's average error ratio of prediction reaches 2.72%. So the GA-LS-SVM model's return-judge accuracy and the average error ratio are better than those of LS-SVM algorithm model whose parameters are set artificially.