Xiaoping Han, Yan-Hong Liu, Xuyuan Zhang, Zhiyong Zhang, Hua Yang
{"title":"基于可见-近红外光谱的鸡蛋分选模型研究","authors":"Xiaoping Han, Yan-Hong Liu, Xuyuan Zhang, Zhiyong Zhang, Hua Yang","doi":"10.1080/21642583.2022.2112317","DOIUrl":null,"url":null,"abstract":"To realize the automatic sorting of eggs, the sorting models are established in this paper by using the visible-near infrared spectroscopy technique and taking the eggshell colour, integrity, as well as the feeding mode as sorting indexes. A variety of methods are selected to remove the noise and systematic error by preprocess the spectral information. The backpropagation neural network (BP), the Principal Component Analysis (PCA) coupled with BP and the Soft Independent Modeling of Class Analogy (SIMCA) sorting method are used to identify the eggshell colours (white, pink, green), eggshell integrity (intact, cracked) and laying hen feeding mode (caged and cage-free) by their characteristic band, respectively. The prediction correlation coefficient (Rv), the prediction mean square error (RMSEP), the prediction standard error (SEP), the recognition rate ( ) and the rejection rate ( ) are used to evaluate the established models. The results show that the established classification models have high prediction accuracy and small errors. The non-destructive testing (NDT) technology has great potential for large-scale intelligent laying hen farms.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Study on egg sorting model based on visible-near infrared spectroscopy\",\"authors\":\"Xiaoping Han, Yan-Hong Liu, Xuyuan Zhang, Zhiyong Zhang, Hua Yang\",\"doi\":\"10.1080/21642583.2022.2112317\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To realize the automatic sorting of eggs, the sorting models are established in this paper by using the visible-near infrared spectroscopy technique and taking the eggshell colour, integrity, as well as the feeding mode as sorting indexes. A variety of methods are selected to remove the noise and systematic error by preprocess the spectral information. The backpropagation neural network (BP), the Principal Component Analysis (PCA) coupled with BP and the Soft Independent Modeling of Class Analogy (SIMCA) sorting method are used to identify the eggshell colours (white, pink, green), eggshell integrity (intact, cracked) and laying hen feeding mode (caged and cage-free) by their characteristic band, respectively. The prediction correlation coefficient (Rv), the prediction mean square error (RMSEP), the prediction standard error (SEP), the recognition rate ( ) and the rejection rate ( ) are used to evaluate the established models. The results show that the established classification models have high prediction accuracy and small errors. The non-destructive testing (NDT) technology has great potential for large-scale intelligent laying hen farms.\",\"PeriodicalId\":46282,\"journal\":{\"name\":\"Systems Science & Control Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2022-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Systems Science & Control Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/21642583.2022.2112317\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems Science & Control Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21642583.2022.2112317","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Study on egg sorting model based on visible-near infrared spectroscopy
To realize the automatic sorting of eggs, the sorting models are established in this paper by using the visible-near infrared spectroscopy technique and taking the eggshell colour, integrity, as well as the feeding mode as sorting indexes. A variety of methods are selected to remove the noise and systematic error by preprocess the spectral information. The backpropagation neural network (BP), the Principal Component Analysis (PCA) coupled with BP and the Soft Independent Modeling of Class Analogy (SIMCA) sorting method are used to identify the eggshell colours (white, pink, green), eggshell integrity (intact, cracked) and laying hen feeding mode (caged and cage-free) by their characteristic band, respectively. The prediction correlation coefficient (Rv), the prediction mean square error (RMSEP), the prediction standard error (SEP), the recognition rate ( ) and the rejection rate ( ) are used to evaluate the established models. The results show that the established classification models have high prediction accuracy and small errors. The non-destructive testing (NDT) technology has great potential for large-scale intelligent laying hen farms.
期刊介绍:
Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory