基于可见-近红外光谱的鸡蛋分选模型研究

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS Systems Science & Control Engineering Pub Date : 2022-08-30 DOI:10.1080/21642583.2022.2112317
Xiaoping Han, Yan-Hong Liu, Xuyuan Zhang, Zhiyong Zhang, Hua Yang
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引用次数: 4

摘要

为了实现鸡蛋的自动分选,本文采用可见光-近红外光谱技术,以蛋壳的颜色、完整性和饲养方式为分选指标,建立了鸡蛋的分选模型。通过对频谱信息进行预处理,选择了多种方法来去除噪声和系统误差。采用反向传播神经网络(BP)、主成分分析(PCA)结合BP和类相似软独立建模(SIMCA)分类方法,分别通过蛋壳颜色(白色、粉红色、绿色)、蛋壳完整性(完整、破裂)和蛋鸡饲养模式(笼式和无笼式)的特征带进行识别。使用预测相关系数(Rv)、预测均方误差(RMSEP)、预测标准误差(SEP),识别率()和拒绝率()来评估所建立的模型。结果表明,所建立的分类模型具有较高的预测精度和较小的误差。无损检测技术在大型智能蛋鸡养殖场具有巨大的应用潜力。
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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.
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
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
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