Fast Spirulina detection with fixed-focus microscope and deep learning

doi:10.1117/12.2651764

	Fast Spirulina detection with fixed-focus microscope and deep learning
	Liu, Hao 1; Lu, Zhen 1; Wang, Yinchu2,3 ; Chen, Ruiquan 1; Cai, Yuwei 1; Xu, Nanlin 1; Peng, Zheng 1; Peng, Xiao 1; Wu, Huifeng4
发表期刊	Proceedings of SPIE - The International Society for Optical Engineering
ISSN	0277786X
	2023
卷号	12563
关键词	Deep learning Food processing Microalgae Viruses
DOI	10.1117/12.2651764
英文摘要	As a kind of microalgae, Spirulina plays an important role in fish culture, food processing industry, medical treatment and bioenergetic development due to its reasonable nutritional composition and high hydrogenase activity. However, the purity of Spirulina, which could be significantly affected by virus infection and miscellaneous algal issues, has great impact on the quality of the product. Thus, periodic Spirulina detection is necessary for quality control of Spirulina culture. Currently, there are two main methods of Spirulina detection: the optical microscopic method and the fluorescence detection method. The former has higher accuracy and a lower speed while the latter has a higher speed in a sample destructing mode. Deep learning-based method has the ability to accelerate data processing. Meanwhile, it can achieve high accuracy by model training and validation. In this work, we have applied deep learning to Spirulina detection to achieve a higher accuracy rate. The process was divided into four main steps: Spirulina culture, image acquisition, image preprocessing and YOLO-v3 model training. The hyperparametric modulation was carried out to determine the appropriate training parameters, providing a trained model with mAP of 0.839 at a detection speed of 20.53 fps. It has great application potential in quantity detection and size detection of cultured Spirulina. © 2023 SPIE.
收录类别	EI
语种	英语
EI主题词	Deep learning ; Food processing ; Microalgae ; Viruses
EI入藏号	20230613559794
引用统计
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/34302
专题	海岸带生物学与生物资源利用重点实验室中国科学院海岸带环境过程与生态修复重点实验室_海岸带环境过程实验室中国科学院海岸带环境过程与生态修复重点实验室海岸带生物学与生物资源利用重点实验室_海岸带生物资源高效利用研究与发展中心
作者单位	1.College of Physics and Optoelectronic Engineering, Shenzhen Key Laboratory of Photonics and Biophotonics, Key Laboratory of Optoelectronic Devices and Systems, Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen; 518060, China; 2.Key Laboratory of Coastal Biology and Biological Resource Utilization, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Yantai; 264003, China; 3.National Basic Science Data Center, Beijing; 100190, China; 4.CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai; 264003, China
推荐引用方式 GB/T 7714	Liu, Hao,Lu, Zhen,Wang, Yinchu,et al. Fast Spirulina detection with fixed-focus microscope and deep learning[J]. Proceedings of SPIE - The International Society for Optical Engineering,2023,12563.
APA	Liu, Hao.,Lu, Zhen.,Wang, Yinchu.,Chen, Ruiquan.,Cai, Yuwei.,...&Wu, Huifeng.(2023).Fast Spirulina detection with fixed-focus microscope and deep learning.Proceedings of SPIE - The International Society for Optical Engineering,12563.
MLA	Liu, Hao,et al."Fast Spirulina detection with fixed-focus microscope and deep learning".Proceedings of SPIE - The International Society for Optical Engineering 12563(2023).