Robotics & Machine Learning Daily News2024,Issue(Jun.27) :2-2.

Data from Jiangsu University Provide New Insights into Machine Learning (Online System for Monitoring the Degree of Fermentation of Oolong Tea Using Integrated Visible-Near-Infrared Spectroscopy and Image-Processing Technologies)

江苏大学的数据为机器学习提供了新的见解(利用可见-近红外光谱和图像处理技术监测乌龙茶发酵程度的在线系统)

Robotics & Machine Learning Daily News2024,Issue(Jun.27) :2-2.

Data from Jiangsu University Provide New Insights into Machine Learning (Online System for Monitoring the Degree of Fermentation of Oolong Tea Using Integrated Visible-Near-Infrared Spectroscopy and Image-Processing Technologies)

江苏大学的数据为机器学习提供了新的见解(利用可见-近红外光谱和图像处理技术监测乌龙茶发酵程度的在线系统)

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摘要

由一名新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-人工智能的新研究是一份新报告的主题。根据NewsRx编辑对镇江的新闻报道,研究表明,"乌龙茶在发酵过程中,其外部特征和内部成分都发生了重大变化"。本研究的资助者包括福建省科技项目。新闻记者引用江苏大学的一篇研究报道:“本研究旨在利用可见-近红外光谱(vis-VIS-NIR)和图像处理技术确定乌龙茶的发酵度,将预处理后的可见-近红外光谱数据经序列提取算法(SPA)特征选择后与图像特征融合,并对传统机器学习和深度学习分类模型进行比较。”采用支持向量机(SVM)和卷积神经网络(CNN)模型,预测集分别为97.14%和95.15%,预测结果表明,VIS-NIR与图像处理相结合,具有快速无损在线检测乌龙茶色素含量的能力。在本研究中,传统机器学习模型的预测率超过了深度学习模型。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on artificial intelligenc e is the subject of a new report. According to news reporting out of Zhenjiang, People’s Republic of China, by NewsRx editors, research stated, “During the ferm entation process of Oolong tea, significant changes occur in both its external c haracteristics and its internal components.” Funders for this research include Scientific And Technological Projects of Fujia n Province. The news reporters obtained a quote from the research from Jiangsu University: “ This study aims to determine the fermentation degree of Oolong tea using visible -near-infrared spectroscopy (vis-VIS-NIR) and image processing. The preprocessed vis-VIS-NIR spectral data are fused with image features after sequential projec tion algorithm (SPA) feature selection. Subsequently, traditional machine learni ng and deep learning classification models are compared, with the support vector machine (SVM) and convolutional neural network (CNN) models yielding the highes t prediction rates among traditional machine learning models and deep learning m odels with 97.14% and 95.15% in the prediction set, respectively. The results indicate that VIS-NIR combined with image processing p ossesses the capability for rapid nondestructive online determination of the fe rmentation degree of Oolong tea. Additionally, the predictive rate of traditiona l machine learning models exceeds that of deep learning models in this study.”

Key words

Jiangsu University/Zhenjiang/People’s Republic of China/Asia/Cyborgs/Emerging Technologies/Machine Learning/Techn ology

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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