Automatic Apple Grading System Based On ANN And Mono-Ocular Three-Views-Synchronized Image Acquisition

Due to poor apple quality and low price in China, and identifying stem/calyx of apple was difficult inapple grading, an apple grading machine and software based on techniques such as machine vision, neuralnetwork, and automatic orientation etc. was developed in this paper to improve the quality of apple in China.Integrated grading technique based on HALCON and C++and neural network was discussed. Method ofcapturing three images at the same time using one camera with two mirrors was researched. The results ofstudy could give basis of manufacturing apple grading production line.The innovative results and conclusions were list as follows:(1) An apple grading machine was designed through autorotation orientation, where apple could beautomatically feed, discharged, and orientated. The stem and calyx of apple was orientated invertical position which could save time of image processing.(2) Automatic preservatives spraying device was innovatively brought forward to be integrated withthe apple grading machine to improve productivity and apple quality, cut the production cost.(3) An image capturing system with one camera and two mirrors was designed to capture threeimages of one apple at the same time. The other four fill-in mirrors were used to reflect light,where clear image would be captured. Compared with other kind of machine vision systems, thesystem in this paper saved at least two cameras, and real-time performance was enhanced also.(4) Error of image capturing was researched. Mathematical model of error of image capturing systemby one camera and two mirrors was established. It was shown that the error of blind area of imagecapturing was only5%which was in the stem and calyx of apple and where didn’t need to detectby designing. Formula of depth of focus was established, so it could get three images of one appleclearly by one capturing.(5) New method of apple color grading was discussed. Using hue component in0～60o and210o～255o to segment apple image. The ratio of the segmentation area by using hue component in0～60o and210o～255o to the whole area of image was introduced as color grading parameter. Thismethod had advantage of fast calculation speed. L*a*b*color mode was discussed to grade applecolor, where value a*b*at0.9~1was top grade, value a*b*at0.8~0.9was first grade, valueless than0.8was second grade.(6) New method to extract apple surface defects based on the Kirsch operator was discussed. Kirsh,LOG, Sobel was used to segment surface detect of apple respectively, where Kirsch operaterextracting surface detects of apple is the best, LOG is the worst.(7) Multi-threading technology was used in apple grading system. The speed of grading was morequickly by using multi-threading technology, more than20apple images could be processedwithin1s, which could meet the needs of real-time production.(8) Integrated grading technique was researched. Integrated grading system based on HALCON andC++and artificial neural network was designed.30apples were graded using this system, gradingaccuracy was96.7%compared with manual grading.