Chunjiang Zhao

National Engineering Research Center for Information Technology in Agriculture

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Wenqian Huang

Beijing University of Chinese Medicine

Jiangbo Li

Guangzhou University

Shuxiang Fan

Beijing Academy of Agricultural and Forestry Sciences

Qingyan Wang

Harbin University of Science and Technology

Xi Tian

North China Electric Power University

Huarui Wu

National Engineering Research Center for Information Technology in Agriculture

Zhiming Guo

Jiangsu University

Liping Chen

National Engineering Research Center for Information Technology in Agriculture

Ting An

Beijing Academy of Agricultural and Forestry Sciences

Daming Dong

Beijing Academy of Agricultural and Forestry Sciences

Cooperative Institutions

National Engineering Research Center for Information Technology in Agriculture

Beijing Academy of Agricultural and Forestry Sciences

Ministry of Agriculture and Rural Affairs

Chinese Academy of Sciences

Beijing Automation Control Equipment Institute

China Agricultural University

Jiangsu University

Northwest A&F University

Shanghai Jiao Tong University

Peking University

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Monitoring the major taste components during black tea fermentation using multielement fusion information in decision level

Food Chemistry X (2023)

Ting An Zheli Wang Guanglin Li Shuxiang Fan Wenqian Huang

Hitherto, the intelligent detection of black tea fermentation quality is still a thought-provoking problem because of one-side sample information and poor model performance. This study proposed a novel method for the prediction of major chemical components including total catechins, soluble sugar and caffeine using hyperspectral imaging technology and electrical properties. The multielement fusion information were used to establish quantitative prediction models. The performance of model using multielement fusion information was better than that of model using single information. Subsequently, the stacking combination model using fusion data combined with feature selection algorithms for evaluating the fermentation quality of black tea. Our proposed strategy achieved better performance than classical linear and nonlinear algorithms, with the correlation coefficient of the prediction set (Rp) for total catechins, soluble sugar and caffeine being 0.9978, 0.9973 and 0.9560, respectively. The results demonstrated that our proposed strategy could effectively evaluate the fermentation quality of black tea.

Black tea

10.1016/j.fochx.2023.100718

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Citations (8)

Automatic detection of defective apples using NIR coded structured light and fast lightness correction

Journal of Food Engineering (2017)

Chi Zhang Chunjiang Zhao Wenqian Huang Qingyan Wang Shenggen Liu

Lightness

Spots

Calyx

RGB color model

Machine Vision

10.1016/j.jfoodeng.2017.02.008

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Citations (41)

Personalized agricultural knowledge services: a framework for privacy-protected user portraits and efficient recommendation

The Journal of Supercomputing (2023)

Huarui Wu Chang Liu Chunjiang Zhao

Abstract In recent years, the increasing demand for knowledge services and the challenges of information overload have posed significant problems in delivering personalized and efficient agricultural knowledge services. This paper presents a comprehensive framework that addresses the issues of vague user positioning, serious privacy leakage, and low efficiency in personalized knowledge services within the national agricultural knowledge intelligent service cloud platform. The proposed framework utilizes privacy-protected user portraits based on generative adversarial nets (GAN) and leverages the TextCNN-LSTM algorithm for agricultural knowledge service prediction. By embedding labels into the algorithm and employing data obfuscation techniques, the framework achieves accurate inference of user behavior while preserving user privacy. Experimental results demonstrate the effectiveness and accuracy of the proposed framework, highlighting its potential for regional precise positioning and recommendation of personalized agricultural knowledge services. Experimental data shows that the average absolute error and root-mean-square error of this method are 1.1997 and 1.4143, respectively, and compared with MLP, TextCNN, and LSTM models, and it has higher prediction accuracy. In recent years, the increasing demand for knowledge services and the challenges of information overload have posed significant problems in delivering personalized and efficient agricultural knowledge services.

User information

10.1007/s11227-023-05557-w

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Citations (3)

A Modified Visual Perception-Based Image Segmentation Method

Lecture notes in electrical engineering (2011)

Chunjiang Zhao

10.1007/978-3-642-19706-2_40

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Citations (0)

Prediction of the diet energy digestion using kernel extreme learning machine: A case study with Holstein dry cows

Computers and Electronics in Agriculture (2020)

Qiang Fu Weizheng Shen Xiaoli Wei Yonggen Zhang Hangshu Xin

10.1016/j.compag.2020.105231

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Citations (22)

Detection of domestic detergent residues on porcelain tableware using laser induced breakdown spectroscopy

RSC Advances (2017)

Xiande Zhao Daming Dong Yang Li Chunjiang Zhao

Domestic detergents are widely used and the detection of detergent residues on tableware is closely related to people's health. Using LIBS to detect detergent rapidly has a promising potential.

Laser-induced breakdown spectroscopy

10.1039/c7ra04304j

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Citations (3)

Analyzing Strawberry Spoilage via its Volatile Compounds Using Longpath Fourier Transform Infrared Spectroscopy

Scientific Reports (2013)

Daming Dong Chunjiang Zhao Wengang Zheng Wenzhong Wang Xiande Zhao

The volatile compounds from fruits vary based on the spoilage stage. We used FTIR spectroscopy to analyze and to attempt to identify the spoilage process of strawberries. To enhance the sensitivity of the measuring system, we increased the optical pathlength by using multi-reflecting mirrors. The volatile compounds that were vaporized from strawberries in different spoilage stages were tested. We analyzed the spectra and found that the concentrations of esters, alcohols, ethylene and similar compounds changed with deterioration. The change patterns of the infrared spectra for the volatiles were further examined using 2D correlation spectroscopy. We analyzed the spectral data using PCA and were able to distinguish the fresh, slightly spoiled strawberries from the seriously spoiled strawberries. This study demonstrates that FTIR is an effective tool for monitoring strawberry spoilage and for providing status alerts.

Fourier transform spectroscopy

10.1038/srep02585

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Citations (47)

A Modified Sobel Edge Detection Using Dempster-Shafer Theory

Chunjiang Zhao Yong Deng

A modified Sobel edge detection is proposed in this paper. Dempster-Shafer theory, also known as the theory of belief function, is applied to improve the drawbacks of the conventional Sobel operator, for instance, the thick edge and sensitive to noise. The reason is that by selecting the mass function, Dempster-Shafer theory can distinguish the edge pixels from the uncertain edge pixels correctly, which can suppress the noise and make the edge thinner. Firstly, the Sobel operator is employed to obtain the gradient magnitude G _x and G _y , which are regarded as the independent sources; and the mass function is selected by the gradient values and the overlapping surface of the constructed triangles; then, the orthogonal sum is calculated; finally, the mass function of the edge probability is taken as the edge image. From the experiment, the edge is thin and noise-free, so the result could be accepted.

Sobel operator

Dempster–Shafer theory

Image gradient

Canny edge detector

Operator (biology)

10.1109/cisp.2009.5305495

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Citations (6)

Classification of heavy metal Cd stress in lettuce leaves based on WPCA algorithm and fluorescence hyperspectral technology

Infrared Physics & Technology (2021)

Xin Zhou Chunjiang Zhao Jun Sun Yan Cao Lvhui Fu

Environmental Pollution