Research on Image Classification Algorithm of Haematococcus Pluvialis Cells

Abstract

Haematococcus pluvialis has the highest content of astaxanthin and is recognized as the best biological source for astaxanthin production in nature. Its culture stage is mainly divided into cell proliferation stage and astaxanthin accumulation stage. Because the color and cell radius of Haematococcus pluvialis cells are different at different stages, an efficient and accurate image classification algorithm is sought to identify the growth stage of Haematococcus pluvialis. In response to this problem, this paper selects three algorithms (C4.5 decision tree, SVM, KNN) with more balanced complexity and accuracy in the machine learning image classification algorithm to identify Haematococcus pluvialis cells, and explores the application of Pluvialis pluvialis cells. Algorithms for image classification of algal cells. The image classification is completed by extracting the features of the cell image and calculating the number of pixels in the image, and then using the corresponding algorithm to train the model. The experimental results show that the decision tree algorithm is significantly better than the other two algorithms, and the classification accuracy is about 97%. It can be seen that the classification model trained by the decision tree algorithm achieves a good classification effect, and provides an effective method for solving the problem of Haematococcus pluvialis cell image classification.