Detection Level of Apple Based on BP Neural Network

In view of the draw backs of apple grade identification in China, which still relies on photoelectric sorting and manual separation, this paper presents a processing method on the basis of the technology of computer vision and digital image.Utilizing image processing technology, the researcher calculated the length of the long-short-axis, marked the location of it and calculated the 4 parameters, color, mean square,shape,size, as the key characteristics of the BP input of network to build a network and identify the level of apple through analysis of the external characteristics of apple. The optimum structure parameters of the BP neural network which had 9 hidden layer neurons were determined by RP training algorithm. Results showed that average accuracy for fruit classification can reach 92.5% by using this model,and the executing time of microcomputer for grading of one apple is 9.3 ms.This method has the characteristics of high accuracy and good real-time performance.