Classification of tea and stalk based on minimum risk Bayesian classifier

Currently,in the process of actual production and processing of tea,the technology of tea-leaf and tea-stalk automational sorting is still in their infancy,and the precision and efficiency of sorting machinery hardly can achieve the desired objective.So the time and manpower costs must be increased again through the prcocess of manual sorting.In this paper,the digital camera is used to collect numeric pictures of tea-leaf and tea-stalk,then the color and shape features of these samples are extracted after pretreatment,and model is built with the use of multi-Gaussian model.The minimum risk Bayes classifier model is used to separate tea-leaf from tea-stalk.Experiments show that the minimum risk-based Bayesian classifier is feasible,and can obtain good classification results.