Abstract
In the early stage of incubation, the infertile eggs still have certain edible value, and if removed earlier, they can not only reduce economic losses, but also avoid the impact on other normally hatched eggs. In order to solve the problem of online detection of group eggs information，this paper uses machine vision technology to detect the fertilization information of group chicken eggs based on industrial egg trays for the first time. The whole eggs were directly put into the detection device from the incubator to obtain the group egg images, which reduced unnecessary damage to the eggs and improves the efficiency，the image was segmented and smoothing denoised，and RGB, HIS, gray mean and egg weight of it were extracted as characteristic parameters by establishing identification models of support vector machine (SVM) and BP neural network. The experimental results show that among the three models, the SVM model has higher stability and accuracy, reaching 81.7% and 96.7% on the 3rd and 7th days respectively, which provides a feasible method for online detection of group egg information.
Publication Date
4-28-2019
First Page
152
Last Page
156
DOI
10.13652/j.issn.1003-5788.2019.04.028
Recommended Citation
Linfeng, WU; Huaixin, YU; and Zhihui, ZHU
(2019)
"Identification group of fertilized eggs and infertile eggs in early hatching stage based on machine vision,"
Food and Machinery: Vol. 35:
Iss.
4, Article 28.
DOI: 10.13652/j.issn.1003-5788.2019.04.028
Available at:
https://www.ifoodmm.cn/journal/vol35/iss4/28
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