# Deep Learning-Based Water Crystal Classification
###### Abstract  
Much of the earth's surface is covered by water. As was pointed out in the 2020 edition of the World Water Development Report, climate change challenges the sustainability of global water resources, so it is important to monitor the quality of water to preserve sustainable water resources. Quality of water can be related to the structure of water crystal, the solid-state of water, so methods to understand water crystals can help to improve water quality. As a first step, a water crystal exploratory analysis has been initiated with the cooperation with the Emoto Peace Project (EPP). The 5K EPP dataset has been created as the first world-wide small dataset of water crystals. Our research focused on reducing the inherent limitations when fitting machine learning models to the 5K EPP dataset. One major result is the classification of water crystals and how to split our small dataset into several related groups. Using the 5K EPP dataset of human observations and past research on snow crystal classification, we created a simple set of visual labels to identify water crystal shapes, in 13 categories. A deep learning-based method has been used to automatically do the classification task with a subset of the label dataset. The classification achieved high accuracy when using a fine-tuning technique. water crystal; deep learning; fine-tuning; supervised; classification +
Footnote †: journal: Medical  
and water crystals. From the shape of the crystal, the purity level and the texture are clearly reflected, which then enables us to assess the quality of the water. Depending on the environmental conditions and the impact of the surrounding elements, the same water can give many different shapes. Each type of shape of crystals can be considered to be unique, without repetition. Many studies have been performed on classifying snow crystals and ice crystals [3; 4; 5; 6]. In recent years, the application of deep learning on crystals became popular in the research world with the publication of 3D Crystal classification [7]. However, no real classification and understanding of water crystals based on deep learning have been completed until now. This article innovation is the application of artificial intelligence for the first time on a dataset of photos of water crystals known as \"the 5K EPP Dataset\" collected with the collaboration of the I.H.M General Research Institute. This dataset is composed of high-resolution photos captured by a microscope camera under laboratory conditions. All the photos have been stored and managed by the I.H.M General Research Institute. A simple water crystal structure definition has been proposed in this research, which references snow crystals classification from [5] and the EPP project. This definition provides an easy understanding of the water structure with a 2D image. With non-prohibited purposes, the water crystal dataset can support other researchers interested in water with their dataset [8; 9]. These results assess the affection of human beings on water crystals initialization and the quality of water. Deep learning has been widely applied in many research fields and achieved surprising results, especially in image processing. Convolutional neural networks (CNNs) are a special kind of neural network analysing high dimensional features dataset such as image, video, etc. CNNs were developed with image processing in mind, which makes them computationally more efficient when compared to other multi-layer back-propagation neural networks. CNNs can be used to automatically extract features from the dataset, which simplifies the next process. These features are not only useful for specific tasks but also can help in other related tasks. This opens a new era for research with reduced effort to achieve good results. With its well-understood architecture, CNNs are nowadays used widely in many areas, including image and structure classification.