Identifying Potato External Damage Based on Hyperspectral Image System and SVM Networks
Identifying potato external damage using hyperspectral image system was explored.The experiment of hyperspectral image was cardied out for external frostbite,mechanical damage,hurt and normal(a total of 162) potato.Principal component analysis was performed to realize data dimensionality reduction based on the original experimental data.The mean,standard deviation,smoothness,third moment,uniformity,entropy of 6 depicts extracted from the dimensionality reduction feature image were used to composite the sub-feature vector.The eigenvector was input separately to bayesian classifier,the BP neural network and SVM neural network model for identification.The results showed that bayesian classifier model seriously misjudged frostbite and mechanical damage potatoes.The recognition rate of BP neural network model was low for mechanical damage type of potato.The SVM neural network model obviously improved recognition rate among the first two models and was the most suitable model for identifying potato external damage.
hyperspectral image systempotato external damageprincipal component analysisbayesian classifiersneural network model
NETL
NSTL
万方数据
维普
