Investigating quantitative approach for microalgal biomass using deep convolutional neural networks and image recognition.

Journal: Bioresource technology
PMID: 38797362

Abstract

The effective monitoring of microalgae cultivation is crucial for optimizing their energy utilization efficiency. In this paper, a quantitative analysis method, using microalgae images based on two convolutional neural networks, EfficientNet (EFF) and residual network (RES), is proposed. Suspension samples prepared from two types of dried microalgae powders, Rhodophyta (RH) and Spirulina (SP), were used to mimic real microalgae cultivation settings. The method's prediction accuracy of the algae concentration ranges from 0.94 to 0.99. RH, with a distinctively pronounced red-green-blue value shift, achieves a higher prediction accuracy than SP. The prediction results of the two algorithms were significantly superior to those of a linear regression. Additionally, RES outperforms EFF in terms of its generalization ability and robustness, which is attributable to its distinct residual block architecture. The RES provides a viable approach for the image-based quantitative analysis.

Authors

  • Yang Peng
    Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA.
  • Shen Yao
    School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, No 1, Daxue Road, Xuzhou, Jiangsu 221116, China.
  • Aoqiang Li
    Jilin Provincial Key Laboratory of Animal Resource Conservation and Utilization, No. 2555, Street Jingyue, Northeast Normal University, Changchun 130117, China.
  • FeiFei Xiong
    School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, No 1, Daxue Road, Xuzhou, Jiangsu 221116, China.
  • Guangwen Sun
    School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, No 1, Daxue Road, Xuzhou, Jiangsu 221116, China.
  • Zhouzhou Li
    School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, No 1, Daxue Road, Xuzhou, Jiangsu 221116, China.
  • Huaichun Zhou
    School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, No 1, Daxue Road, Xuzhou, Jiangsu 221116, China.
  • Yang Chen
    Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China.
  • Xun Gong
    School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, PR China; Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Chengdu 611756, PR China; Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu 611756, PR China. Electronic address: xgong@swjtu.edu.cn.
  • Fanke Peng
    School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, No 1, Daxue Road, Xuzhou, Jiangsu 221116, China.
  • Zhuolin Liu
    School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, No 1, Daxue Road, Xuzhou, Jiangsu 221116, China.
  • Chuxuan Zhang
    State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan 430074, China.
  • Jianhui Zeng
    State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan 430074, China.

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