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Zhang H, Pan Y, Liu X, Chen Y, Gong X, Zhu J, Yan J, Zhang H. Recognition of the rhizome of red ginseng based on spectral-image dual-scale digital information combined with intelligent algorithms. Spectrochim Acta A Mol Biomol Spectrosc 2023; 297:122742. [PMID: 37098315 DOI: 10.1016/j.saa.2023.122742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/08/2023] [Accepted: 04/11/2023] [Indexed: 05/14/2023]
Abstract
Red ginseng is a widely used and extensively researched food and medicinal product with high nutritional value, derived from steamed fresh ginseng. The components in various parts of red ginseng differ significantly, resulting in distinct pharmacological activities and efficacies. This study proposed to establish a hyperspectral imaging technology combined with intelligent algorithms for the recognition of different parts of red ginseng based on the dual-scale of spectrum and image information. Firstly, the spectral information was processed by the best combination of first derivative as pre-processing method and partial least squares discriminant analysis (PLS-DA) as classification model. The recognition accuracy of the rhizome and the main root of red ginseng is 96.79% and 95.94% respectively. Then, the image information was processed by the You Only Look Once version 5 small (YOLO v5s) model. The best parameter combination is epoch = 30, learning rate = 0.01, and activation function is leaky ReLU. In the red ginseng dataset, the highest accuracy, recall and mean Average Precision at IoU (Intersection over Union) threshold 0.5 (mAP@0.5) were 99.01%, 98.51% and 99.07% respectively. The application of spectrum-image dual-scale digital information combined with intelligent algorithms in the recognition of red ginseng is successful, which provides a positive significance for the online and on-site quality control and authenticity identification of crude drugs or fruits.
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Affiliation(s)
- HongXu Zhang
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China
| | - YiXia Pan
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China
| | - XiaoYi Liu
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China
| | - Yuan Chen
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China
| | - XingChu Gong
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - JieQiang Zhu
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China
| | - JiZhong Yan
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China.
| | - Hui Zhang
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China.
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Hou C, Zhang X, Tang Y, Zhuang J, Tan Z, Huang H, Chen W, Wei S, He Y, Luo S. Detection and localization of citrus fruit based on improved You Only Look Once v5s and binocular vision in the orchard. Front Plant Sci 2022; 13:972445. [PMID: 35968138 PMCID: PMC9372459 DOI: 10.3389/fpls.2022.972445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 07/12/2022] [Indexed: 06/15/2023]
Abstract
Intelligent detection and localization of mature citrus fruits is a critical challenge in developing an automatic harvesting robot. Variable illumination conditions and different occlusion states are some of the essential issues that must be addressed for the accurate detection and localization of citrus in the orchard environment. In this paper, a novel method for the detection and localization of mature citrus using improved You Only Look Once (YOLO) v5s with binocular vision is proposed. First, a new loss function (polarity binary cross-entropy with logit loss) for YOLO v5s is designed to calculate the loss value of class probability and objectness score, so that a large penalty for false and missing detection is applied during the training process. Second, to recover the missing depth information caused by randomly overlapping background participants, Cr-Cb chromatic mapping, the Otsu thresholding algorithm, and morphological processing are successively used to extract the complete shape of the citrus, and the kriging method is applied to obtain the best linear unbiased estimator for the missing depth value. Finally, the citrus spatial position and posture information are obtained according to the camera imaging model and the geometric features of the citrus. The experimental results show that the recall rates of citrus detection under non-uniform illumination conditions, weak illumination, and well illumination are 99.55%, 98.47%, and 98.48%, respectively, approximately 2-9% higher than those of the original YOLO v5s network. The average error of the distance between the citrus fruit and the camera is 3.98 mm, and the average errors of the citrus diameters in the 3D direction are less than 2.75 mm. The average detection time per frame is 78.96 ms. The results indicate that our method can detect and localize citrus fruits in the complex environment of orchards with high accuracy and speed. Our dataset and codes are available at https://github.com/AshesBen/citrus-detection-localization.
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Affiliation(s)
- Chaojun Hou
- Academy of Contemporary Agriculture Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Xiaodi Zhang
- Academy of Contemporary Agriculture Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Yu Tang
- Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Jiajun Zhuang
- Academy of Contemporary Agriculture Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Zhiping Tan
- Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Huasheng Huang
- Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Weilin Chen
- School of Mechatronics Engineering and Automation, Foshan University, Foshan, China
| | - Sheng Wei
- Engineering Research Center for Intelligent Robotics, Jihua Laboratory, Foshan, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Shaoming Luo
- School of Mechatronics Engineering and Automation, Foshan University, Foshan, China
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