Nondestructive identification for gender of chicken eggs based on GA-BPNN with double hidden layers

SUMMARY: In order to identify the gender of chicken eggs at the early stage of incubation, a machine vision image acquisition system was constructed. Under the light source of LED, the images of 2 batches (186 and 180) of chicken eggs were respectively obtained on d 3, d 4, d 5, d 6, d 8, and d 10 o...

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Autores principales:	Z.H. Zhu, Z.F. Ye, Y. Tang
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	machine vision genetic algorithm BP neural network early stage of incubation chicken eggs gender identification Animal culture SF1-1100 Food processing and manufacture TP368-456
Acceso en línea:	https://doaj.org/article/75e7b16751f3447c9e427a125de19a88
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spelling	oai:doaj.org-article:75e7b16751f3447c9e427a125de19a882021-11-22T04:19:16ZNondestructive identification for gender of chicken eggs based on GA-BPNN with double hidden layers1056-617110.1016/j.japr.2021.100203https://doaj.org/article/75e7b16751f3447c9e427a125de19a882021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1056617121000660https://doaj.org/toc/1056-6171SUMMARY: In order to identify the gender of chicken eggs at the early stage of incubation, a machine vision image acquisition system was constructed. Under the light source of LED, the images of 2 batches (186 and 180) of chicken eggs were respectively obtained on d 3, d 4, d 5, d 6, d 8, and d 10 of incubation. Considering the clarity and the integrity of blood vessels in the field of machine vision, the image of d 4 was determined as the basis for gender identification of chick embryos. After image processing, the 11 dimensions of feature parameters depicting the chick's embryonic development were extracted. In this paper, the genetic algorithm (GA) was used to optimize the initial weights and thresholds of backpropagation neural networks (BPNN) with different hidden layers. Then the GA-BPNN with single hidden layer, as well as, double hidden layers was established respectively. According to the research, the comprehensive accuracy of GA-BPNN model with double hidden layers reached 89.74% for the prediction set, which was higher than that of the model with single hidden layer, indicating that optimizing the initial weights and thresholds of BPNN by GA and adding the hidden layer had a certain effect on improving the recognition accuracy. Meanwhile, the results showed that the machine vision technology provided a feasible method for gender identification of chicken eggs at the early stage of incubation.Z.H. ZhuZ.F. YeY. TangElsevierarticlemachine visiongenetic algorithmBP neural networkearly stage of incubationchicken eggsgender identificationAnimal cultureSF1-1100Food processing and manufactureTP368-456ENJournal of Applied Poultry Research, Vol 30, Iss 4, Pp 100203- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine vision genetic algorithm BP neural network early stage of incubation chicken eggs gender identification Animal culture SF1-1100 Food processing and manufacture TP368-456
spellingShingle	machine vision genetic algorithm BP neural network early stage of incubation chicken eggs gender identification Animal culture SF1-1100 Food processing and manufacture TP368-456 Z.H. Zhu Z.F. Ye Y. Tang Nondestructive identification for gender of chicken eggs based on GA-BPNN with double hidden layers
description	SUMMARY: In order to identify the gender of chicken eggs at the early stage of incubation, a machine vision image acquisition system was constructed. Under the light source of LED, the images of 2 batches (186 and 180) of chicken eggs were respectively obtained on d 3, d 4, d 5, d 6, d 8, and d 10 of incubation. Considering the clarity and the integrity of blood vessels in the field of machine vision, the image of d 4 was determined as the basis for gender identification of chick embryos. After image processing, the 11 dimensions of feature parameters depicting the chick's embryonic development were extracted. In this paper, the genetic algorithm (GA) was used to optimize the initial weights and thresholds of backpropagation neural networks (BPNN) with different hidden layers. Then the GA-BPNN with single hidden layer, as well as, double hidden layers was established respectively. According to the research, the comprehensive accuracy of GA-BPNN model with double hidden layers reached 89.74% for the prediction set, which was higher than that of the model with single hidden layer, indicating that optimizing the initial weights and thresholds of BPNN by GA and adding the hidden layer had a certain effect on improving the recognition accuracy. Meanwhile, the results showed that the machine vision technology provided a feasible method for gender identification of chicken eggs at the early stage of incubation.
format	article
author	Z.H. Zhu Z.F. Ye Y. Tang
author_facet	Z.H. Zhu Z.F. Ye Y. Tang
author_sort	Z.H. Zhu
title	Nondestructive identification for gender of chicken eggs based on GA-BPNN with double hidden layers
title_short	Nondestructive identification for gender of chicken eggs based on GA-BPNN with double hidden layers
title_full	Nondestructive identification for gender of chicken eggs based on GA-BPNN with double hidden layers
title_fullStr	Nondestructive identification for gender of chicken eggs based on GA-BPNN with double hidden layers
title_full_unstemmed	Nondestructive identification for gender of chicken eggs based on GA-BPNN with double hidden layers
title_sort	nondestructive identification for gender of chicken eggs based on ga-bpnn with double hidden layers
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/75e7b16751f3447c9e427a125de19a88
work_keys_str_mv	AT zhzhu nondestructiveidentificationforgenderofchickeneggsbasedongabpnnwithdoublehiddenlayers AT zfye nondestructiveidentificationforgenderofchickeneggsbasedongabpnnwithdoublehiddenlayers AT ytang nondestructiveidentificationforgenderofchickeneggsbasedongabpnnwithdoublehiddenlayers
_version_	1718418229528887296

Nondestructive identification for gender of chicken eggs based on GA-BPNN with double hidden layers

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