Egg Crack Detection Based on Support Vector Machine

Abstract

with the development of agricultural intelligence, it is of great significance to detect egg quality by machine vision and support vector machine in the field of food safety. In order to solve the problems of low efficiency and low accuracy of existing detection methods for egg crack detection, this paper proposes an egg crack detection and recognition method based on support vector machine and machine vision. The feature parameters of egg crack image are extracted by gray scale conversion, median filtering, linear sharpening, threshold segmentation and other means, and the support vector machine model is established, and the model is used to identify and detect eggs. The experimental results show that the model can distinguish intact eggs from cracked eggs, and the detection accuracy of cracked eggs is 98.75%.