Abstract：
Accurately classifying microalgae species is vital for monitoring marine ecosystems and managing the emergence of marine mucilage, which is crucial for monitoring mucilage phenomena in marine environments. Traditional methods have been inadequate due to time-consuming processes and the need for expert knowledge. The purpose of this article is to employ convolutional neural networks (CNNs) and support vector machines (SVMs) to improve classification accuracy and efficiency. By employing advanced computational techniques, including MobileNet and GoogleNet models, alongside SVM classification, the study demonstrates significant advancements over conventional identification methods. In the classification of a dataset consisting of 7820 images using four different SVM kernel functions, the linear kernel achieved the highest success rate at 98.79 %. It is followed by the RBF kernel at 98.73 %, the polynomial kernel at 97.84 %, and the sigmoid kernel at 97.20 %. This research not only provides a methodological framework for future studies in marine biodiversity monitoring but also highlights the potential for real-time applications in ecological conservation and understanding mucilage dynamics amidst climate change and environmental pollution.
摘要：
对微藻物种进行准确分类对于监测海洋生态系统和管理海洋粘液的出现至关重要，这对于监测海洋环境中的粘液现象至关重要。由于耗时的过程和对专家知识的需求，传统方法已经不足。本文的目的是采用卷积神经网络（CNN）和支持向量机（SVM）来提高分类精度和效率。通过采用先进的计算技术，包括MobileNet和GoogleNet模型，与SVM分类一起，这项研究表明，与传统的识别方法相比，有了显著的进步。在使用四种不同的SVM核函数对由7820图像组成的数据集进行分类时，线性内核的成功率最高，为98.79%。其次是RBF内核，占98.73%，多项式内核为97.84%，乙状核为97.20%。这项研究不仅为海洋生物多样性监测的未来研究提供了方法论框架，而且还强调了在生态保护和了解气候变化和环境污染中的粘液动态方面实时应用的潜力。
