Published in Journal 31, 2015
he resemblance between edible mushroom and poisonous mushroom in appearance makes it hard to distinguish them from each other by conventional methods. In order to achieve the automation of judgment and strengthen the reliability, this paper proposed a method to measure the toxicity of mushroom based on support vector machine. To begin with, collection and pre-processing of the sample data were conducted. Then C-SVM model was built up and trained in accordance with one-to-one principle to further achieve multiclassification by support vector machine. At last, constant step length method was applied to obtain the optimum parameters of the model. By comparing accuracy of SVM classification in diverse sample sizes and parameters, the feasibility was verified in simulation experiments. SVM was more accurate, easy-conducting and practical comparing with neural network and decision tree.
Recommended citation: Fan, Ge, et al. "Discriminant Method of Mushroom Toxicity Based on Support Vector Machine." Chinese Agricultural Science Bulletin 31(19):232-236, 2015. [PDF]