Water is an essential resource that facilitates the existence of human life forms. In recent years, the demand for the consumption of freshwater has substantially increased. Seawater contains a high concentration of salt particles and salinity, making it unfit for consumption and domestic use. Water treatment plants used to treat seawater are less efficient and reliable. Deep learning systems can prove to be efficient and highly accurate in analyzing salt particles in seawater with higher efficiency that can improve the performance of water treatment plants. Therefore, this work classified different concentrations of salt particles in water using convolutional neural networks with the implementation of transfer learning. Salt salinity concentration images were captured using a designed Raspberry Pi based model and these images were further used for training purposes. Moreover, a data augmentation technique was also employed for the state-of-the-art results. Finally, a deep learning neural network was used to classify saline particles of varied concentration range images. The experimental results show that the proposed approach exhibited superior outcomes by achieving an overall accuracy of 90% and f-score of 87% in classifying salt particles. The proposed model was also evaluated using other evaluation metrics such as precision, recall, and specificity, and showed robust results.

中文翻译：

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基于深度学习的海水中盐渍颗粒分类方法

水是促进人类生命形式生存的重要资源。近年来，对淡水消耗的需求已大大增加。海水中含有高浓度的盐分和盐分，因此不适合消费和家庭使用。用于处理海水的水处理厂效率和可靠性较低。深度学习系统可以高效，高度准确地分析海水中的盐分，从而提高水处理厂的性能。因此，这项工作使用卷积神经网络并实现了转移学习，对水中不同浓度的盐颗粒进行了分类。使用设计的基于Raspberry Pi的模型捕获盐度盐度浓度图像，并将这些图像进一步用于训练目的。此外，数据增强技术也被用于最新的结果。最后，使用深度学习神经网络对浓度范围变化图像中的盐水颗粒进行分类。实验结果表明，通过对盐颗粒进行分类，总体精度达到90％，f得分达到87％，该方法展现出了优异的结果。还使用其他评估指标（例如精度，召回率和特异性）对提议的模型进行了评估，并显示了可靠的结果。深度学习神经网络用于对浓度范围变化图像中的盐水颗粒进行分类。实验结果表明，通过对盐颗粒进行分类，总体精度达到90％，f得分达到87％，该方法展现出了优异的结果。还使用其他评估指标（例如精度，召回率和特异性）对提议的模型进行了评估，并显示了可靠的结果。深度学习神经网络用于对浓度范围变化图像中的盐水颗粒进行分类。实验结果表明，通过对盐颗粒进行分类，总体精度达到90％，f得分达到87％，该方法展现出了优异的结果。还使用其他评估指标（例如精度，召回率和特异性）对提议的模型进行了评估，并显示了可靠的结果。