Algae are photosynthetic eukaryotes that may range from unicellular to multicellular forms. Algae have been reported from almost all the ecological systems, including terrestrial, marine, and aquatic ecosystems. The manual classification of algae is a time-consuming method and requires great efforts with expertise due to the numerous families and genera. In the present study, an automated system is developed for the identification and classification of the 16 algal families with a data set of 80,000 images by a modified ResNeXt CNN (Convolution Neural Network) model. Cell differentiation by modified ResNeXt CNN topology is based on cell arrangement and morphological features including area, width, shape, and length of the cell. An experimental result of 99.97% classification accuracy demonstrates the effectiveness of the proposed method. The present investigation may open a new path in the future for the development of a time and a cost-effective, highly sensitive computer-based system for the identification and classification of different algae.

藻类是光合作用的真核生物，范围从单细胞形式到多细胞形式。几乎所有生态系统（包括陆地，海洋和水生生态系统）都报告有藻类。手动分类藻类是一种耗时的方法，由于家族和属众多，因此需要大量的专业知识。在本研究中，开发了一种自动系统，用于通过改良的ResNeXt CNN（卷积神经网络）模型对80个图像的数据集进行16个藻类家族的识别和分类。通过改良的ResNeXt CNN拓扑进行细胞分化的依据是细胞排列和形态特征，包括细胞的面积，宽度，形状和长度。分类精度为99.97％的实验结果证明了该方法的有效性。