Research on peanut seeds selection based on color segmentation and PSO-RELM algorithm
Aiming at the problems of large workload and low efficiency in the process of peanut seeds manual selection,a peanut seed selection algorithm based on color segmentation and improved ELM was put forward.Based on clustering characteristics of peanut image,peanut image was segmented by limiting the color range in RGB and HSV color space,to obtain the peanut seeds of image target area.The feature of peanut image was described by using color,shape,improvement of HU moment,based on improved HU moment translation,rotation and scaling invariance,to expand the image characteristics of quantity and get the peanut image data sets.Golden section method was used to select number of hidden layer neurons quickly.Introducing the regularization parameter,the weights matrix stability was improved of the ELM algorithm between neurons in hidden layer and output layer connection.Using PSO algorithm to obtain the optimal input weights and threshold of the hidden layer neurons,on the basis,the PSO-RELM algorithm model was set up,and comparing with BP,ELM,RELM algorithm.Experimental results showed that PSO-RELM had a high recognition accuracy,not only for the intact peanut(100%),but also for the damage peanut(96.71%),average test time was 0.006 8 s,root mean square error was 0.052 0,determination coefficient was 0.987 4,which can meet the real-time requirements for peanut seeds.
NETL
NSTL
万方数据
