A prediction model for growth of Haematococcus pluvialis based on CART algorithm

Xiaoli Wang; Yongli Zhang; Shigang Cui; Jiping Su

doi:10.1117/12.2659656

30 November 2022 A prediction model for growth of Haematococcus pluvialis based on CART algorithm

Xiaoli Wang, Yongli Zhang, Shigang Cui, Jiping Su

Proceedings Volume 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022); 1245616 (2022) https://doi.org/10.1117/12.2659656
Event: International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 2022, Qingdao, China

Abstract

In order to study the growth status of Haematococcus pluvialis in the cell proliferation stage, BG11 was selected as the culture medium, and the light intensity, pH and cell radius were taken as the characteristic variables of the model, and the growth status of algal cells was taken as the target variables of the model to establish a regression prediction model.Through the relevant data of the cell proliferation stage of Haematococcus pluvialis measured in the experiment, the regression tree prediction model is adopted, and the cart algorithm is used for data mining. The growth status of Haematococcus pluvialis is predicted according to the relevant indicators, and finally the growth prediction model of Haematococcus pluvialis is obtained. The use of this model can effectively predict the growth of Haematococcus pluvialis cells, provide a unique idea for the efficient culture of Haematococcus pluvialis, and have a certain guiding significance for the large-scale culture of Haematococcus pluvialis.

Citation Download Citation

Xiaoli Wang, Yongli Zhang, Shigang Cui, and Jiping Su "A prediction model for growth of Haematococcus pluvialis based on CART algorithm", Proc. SPIE 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 1245616 (30 November 2022); https://doi.org/10.1117/12.2659656

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KEYWORDS

Data modeling

Detection and tracking algorithms

Performance modeling

Image segmentation

Machine learning

Binary data

Data mining

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