计算机应用与软件2025，Vol.42Issue(4)：174-180,270,8.DOI:10.3969/j.issn.1000-386x.2025.04.026
基于深度学习的鸡种蛋早期受精信息无损检测
NON-DESTRUCTIVE DETECTION OF EARLY FERTILIZATION INFORMATION OF CHICKEN EGGS BASED ON DEEP LEARNING
摘要
Abstract
In order to solve the problems of late time and high work intensity in the detection of eggs without sperm,a VGG16 network model was improved and a graphical user interface was developed for hatching 2.5 d eggs.The image of hatching 2.5 d eggs was collected by a self-made static image acquisition device.The improved model achieved 98.82％discrimination accuracy and 97.23％recall rate on the enhanced test set,and the detection time of single image was 97.56 ms.Compared with the original network,the recognition accuracy was improved by 5.56 percentage points,and the recognition time of single image was saved by 14.78 ms.The results show that the improved model can effectively realize the nondestructive identification of egg fertilization information in the early stage of incubation,which provides technical support for the subsequent development of online nondestructive testing devices.关键词
计算机应用技术/鸡种蛋/受精信息/无损检测/卷积神经网络/深度学习Key words
Computer application techonology/Chicken eggs/Fertilization information/Non-destructive detection/Convolutional neural networks/Deep learning分类
信息技术与安全科学引用本文复制引用
柳洋洋,崔德建,贾伟娥,夏元天,连正兴,李林..基于深度学习的鸡种蛋早期受精信息无损检测[J].计算机应用与软件,2025,42(4):174-180,270,8.基金项目
国家重点研发计划项目(2016YFD0300710). （2016YFD0300710）
