Nie Ying, Chen Yuqiang, Guo Jianlan, Li Shufei, Xiao Yu, Gong Wendong, Lan Ruirong
School of Intelligent Manufacturing and Information, GuangDong Country Garden Polytechnic, QingYuan, 511500, GuangDong, China.
School of Mechanical Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, 14300, Malaysia.
Sci Rep. 2025 Apr 2;15(1):11264. doi: 10.1038/s41598-025-93521-4.
Water turbidity is an important indicator for evaluating water clarity and plays an important role in environmental protection and ecological balance. Due to the subtle changes in water turbidity images, the differences captured are often too subtle to be classified. Convolutional neural networks (CNN) are widely used in image classification and perform well in feature extraction and classification. This study explored the application of convolutional neural networks in water turbidity classification. The innovation lies in applying CNN to water turbidity images, focusing on optimizing the CNN model to improve prediction accuracy and efficiency. The study proposed four CNN models for water turbidity classification based on artificial intelligence, and adjusted the number of model layers to improve prediction accuracy. Experiments were conducted on noise-free and noisy datasets to evaluate the accuracy and running time of the models. The results show that the CNN-10 model with a dropout layer has a classification accuracy of 96.5% under noisy conditions. This study has opened up new applications of CNN in fine-grained image classification, and further demonstrated the effectiveness of convolutional neural networks in water turbidity image classification through experiments.
水体浑浊度是评估水体清澈程度的重要指标，在环境保护和生态平衡中发挥着重要作用。由于水体浑浊度图像的变化细微，所捕捉到的差异往往过于微妙而难以分类。卷积神经网络（CNN）在图像分类中被广泛应用，在特征提取和分类方面表现出色。本研究探讨了卷积神经网络在水体浑浊度分类中的应用。创新之处在于将CNN应用于水体浑浊度图像，重点优化CNN模型以提高预测准确性和效率。该研究基于人工智能提出了四种用于水体浑浊度分类的CNN模型，并调整模型层数以提高预测准确性。在无噪声和有噪声的数据集上进行了实验，以评估模型的准确性和运行时间。结果表明，带有随机失活层的CNN - 10模型在有噪声条件下的分类准确率为96.5%。本研究开辟了CNN在细粒度图像分类中的新应用，并通过实验进一步证明了卷积神经网络在水体浑浊度图像分类中的有效性。
