# Convolutional Neural Network for Measurement of Suspended Solids and Turbidity
## 1 Introduction  
In aquatic environments such as intensive aquaculture systems, there is an accumulation of organic and inorganic matter, feed residues, and aquatic microorganisms [1]. This accumulation is associated with total suspended solids (mgL\({}^{-1}\)) and could be defined as the mass present in a water column. Suspended particles scatter and absorb light, causing turbidity or loss of transparency of the water [2]. Total suspended solids (TSS) is an important parameter in determining water quality. In aquaculture, a high level of TSS reduces the vision and ability of fish to catch their feed. Mathematically, a TSS value can be correlated with turbidity [3], which uses the degree of loss of water transparency due to suspendedsolids. Therefore, the greater the number of suspended solids in the liquid, the greater the degree of turbidity [4]. For turbidity measurement, an established protocol is method 180.1 by the U.S EPA. The measurement ranges between 0 and 40 NTU (nephelometric turbidity units), and to achieve higher values, the samples must be diluted in water and the measurement must be rescaled. Otherwise, there are several arrangements for turbidimeters that use different light sources and detectors. However, none of them can be used as a low-cost alternative to monitor water quality in a rapid noninvasive way with a wide dynamic range. For example, some low-cost turbidimeters operate with short ranges; their operating ranges are 2.2-54.2 [5], 0-10 [6], 0-12 [7], and 0-100 NTU [8; 9; 10]. Furthermore, these methods do not allow the estimation of water turbidity using a single data record. There are advanced turbidimeters with a wide operating range, but they are expensive and require two data records for turbidity estimation [6; 11]. By contrast, recent advances in computer vision, software development, and other technological advances are being implemented to solve some measurement limitations (the lack of a wide operating range, high cost, etc.) of conventional turbidimeters. Recent advances in turbidimeters are focused on image analysis. Examples include the novel turbidimeter technique of [PERSON] et al. that developed a turbidimeter based on image degradation analysis; however, this method needs reference samples, and samples need additional treatment (must be sonicated) [12]. [PERSON] et al. applied a random forest ensemble to space remote sensing data to obtain river turbidity measurement based on hyperspectral images, and obtained precision of 67% [13]. [PERSON] et al. carried out turbidity measurements in the range 10-250 NTU using image processing methods, where they processed and analyzed image-by-image in the measurement process, reaching a precision of 90% with controlled environmental conditions [14]. In addition, with the advent of smartphones, new turbidimeters have been reported. In the case of [PERSON] et al., they performed turbidity measurements using a smartphone; however, the precision between the samples measured with the calibrated Hach colorimeter and their smartphone colorimeter was 48% [15]. [PERSON] and [PERSON] also measured turbidity using a smartphone by remotely detecting water reflectance based on environmental conditions, reaching precision of 74% [16]. In this study, we measure turbidity and suspended solids by using a CNN that could be used as a low-cost alternative to monitor water quality in a rapid noninvasive way with a wide dynamic range. Our research proposes to use revolutionary emerging technology called convolutional neural network (CNN) and open-source technologies to develop a technique capable of measuring TSS and turbidity values simultaneously from a single image of the liquid sample to be measured. CNN is inspired by the working of the human brain and is able to analyze raw data without human intervention. CNNs have been applied successfully in different areas such as images classification [17; 18], image segmentation [19], roughness measurement [20; 21], and soft sensors [22; 23; 24].