Image-based Detection Method Of Kyoho Grape Fruit Size Research

Grape is an important fruit widely grown in China and ranks second among all fruit production.As the most common grape variety on the market in China,Kyoho grape often has different fruit size on the ear,which seriously affects the appearance quality of the grape.With the increase of production and the improvement of the quality of life,people’s requirements for the external quality of grapes are getting higher and higher.The external quality inspection of Kyoho grapes is an important means to improve their market competitiveness.The existing detection standards are mostly directed to grape ears.The overall quality of the ear can only be described by weight and coloring rate.The quality of the fruit in the ear can not be described,and the limitation is large.The quality measurement of Kyoho grape has become the focus of current research.The accurate measurement of grape fruit size is the basis for the quality measurement of grape fruit.Combining image segmentation,ellipse fitting and camera calibration,this paper proposes a grape fruit size detection method based on grape image.In the traditional fruit image detection principle and implementation method,combined with the characteristics of grape ear and the actual shooting environment,the image segmentation algorithm used in the detection process and the fitting method of the elliptical outer edge contour are optimized,and two optimized algorithms are used.The size calculation of the grape fruit is realized by camera calibration technology.The main work of this paper is as follows.In order to improve the quality of grape images and reduce the effects of noise and uneven illumination during acquisition and transmission,this paper preprocesses the acquired images.By comparing the denoising effect of the mean filtering,the weighted average filtering and the median filtering on the grape image,the median filtering is selected to denoise the image,and the color image logarithmic processing framework(LIPC)is used to equalize the brightness spatial distribution of the image.Aiming at the problem of poor segmentation accuracy of grape images under complex background,this paper proposes an image segmentation algorithm based on local anomaly factor(LOF)algorithm and K-means algorithm.First,the local deviation factor value is calculated for the data set composed of pixel points,then the data set is reordered by the local deviation factor value,the largest n first objects are eliminated,and finally the K-is output to the data set output by the local anomaly factor algorithm.Means algorithm,and in the clustering process,select the pixel with large local deviation factor value as the initial clustering center.The algorithm reduces the clustering error caused by the initial cluster center selection deviation point,and reduces the influence of outliers and isolated points on the clustering results.Existing elliptical fitting methods can only be applied to the detection of one or a small number of ellipses in an image.In this paper,an ellipse detection method based on gradient features is proposed.Combining pixel point gradient features to divide all the pixels in the image into different sets,and applying the random transform ellipse detection algorithm(RED)to the ellipse fitting in the set.It improves the problems of missed detection,false detection and the deviation of the fitting result from the actual ellipse often caused by the traditional fitting method for multiple elliptical target detection of complex images.Distortion correction of the collected grape ear image by camera calibration technology,and using the black and white checkerboard as a reference to convert the grape fruit pixel size in the image into the actual size of the grape fruit,by the mean of the fruit size on the ear and The variance describes the appearance quality of the fruit.