For identification of different Pediastrum species in a sample, the determination of microscopic feature and colony morphology are the preliminary steps before sending them to the higher genomic and proteomic level. Great efforts with high expertise are required for the time-consuming manual process. In the present study, the first time an effort has been done to address the problem for identification and classification of Pediastrum species with the help of convolutional neural networks (CNNs). The modified ResNeXt CNN (Convolution Neural Network) model is used for training and validation of the data set consisting of 42,000 algal images. Modified ResNeXt CNN topology differentiates cells based on the formation of coenobia, cell arrangement and feature and particularly the sculptures on the outer sporopollenin cell-wall layers. An experimental result of 98.45% classification accuracy and F1-score more than 0.98 demonstrates the effectiveness of the proposed method. In the future, such time and cost-effective facilities can be used as promising sources for phycological studies.

为了鉴定样品中的不同Pediastrum种类，在将它们送入更高的基因组和蛋白质组学水平之前，要确定微观特征和菌落形态是初步步骤。耗时的手动过程需要高度专业的付出。在本研究中，第一次努力解决了鉴别和分类书皮的问题卷积神经网络（CNN）的帮助。改进的ResNeXt CNN（卷积神经网络）模型用于训练和验证由42,000个藻类图像组成的数据集。改良的ResNeXt CNN拓扑可根据结肠的形成，细胞排列和特征（尤其是孢子粉外细胞壁层上的雕塑）区分细胞。分类精度为98.45％，F1分数大于0.98的实验结果证明了该方法的有效性。将来，这样的时间和具有成本效益的设施可以用作有希望的生理研究来源。