# Convolutional Neural Network for Measurement of Suspended Solids and Turbidity
## 4 Conclusions  
In this paper, a novel method was proposed to estimate TSS and turbidity values in liquid samples using a CNN and an MLR together with a smartphone. The main conclusions of the study follow:  
* The CNN and the MLR developed can estimate the TSS and turbidity values using images recorded by a common smartphone (Android and IOS systems). * The proposed method is capable of estimating concentration values for unknown classes (validated with samples of unknown concentrations for the CNN). * The proposed method is capable of estimating TSS and turbidity measurements in homogeneous material samples with different particle size, which are in motion or suspended. The use of a mixer is to keep all the particles suspended, for preventing them from settling, and to allow for the suspended particles to be recorded by the camera. * This method of measuring turbidity and TSS is inexpensive and reduces human intervention. The results show the effectiveness of the proposed method, indicating high accuracy of 98.24% and 97.20% for TSS and turbidity measurements, respectively, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**Turbidity** **(NTU)**} & \multicolumn{4}{c}{**Colors**} \\ \cline{3-6}  & & **White** & **Red** & **Blue** & **Green** \\ \hline \multirow{6}{*}{**Cursurs**} & 12 & 12.69 \(\pm\) 2.97 & 6.31 \(\pm\) 2.94 & 1.16 \(\pm\) 13.63 & 3.56 \(\pm\) 6.88 \\  & 42 & 39.90 \(\pm\) 4.83 & 31.48 \(\pm\) 3.99 & 21.89 \(\pm\) 13.20 & 41.51 \(\pm\) 15.12 \\  & 73 & 71.55 \(\pm\) 4.32 & 61.46 \(\pm\) 5.56 & 52.53 \(\pm\) 13.25 & 48.35 \(\pm\) 13.20 \\  & 105 & 99.43 \(\pm\) 4.59 & 91.05 \(\pm\) 4.88 & 89.44 \(\pm\) 15.68 & 80.51 \(\pm\) 13.12 \\  & 134 & 132.30 \(\pm\) 2.91 & 111.68 \(\pm\) 10.82 & 128.61 \(\pm\) 10.74 & 97.92 \(\pm\) 10.77 \\  & 156 & 153.95 \(\pm\) 4.05 & 136.04 \(\pm\) 8.20 & 143.68 \(\pm\) 10.80 & 137.89 \(\pm\) 10.69 \\  & 207 & 199.26 \(\pm\) 4.91 & 170.15 \(\pm\) 10.93 & 179.04 \(\pm\) 13.47 & 166.02 \(\pm\) 12.96 \\  & 272 & 254.33 \(\pm\) 5.05 & 307.24 \(\pm\) 8.04 & 305.88 \(\pm\) 12.28 & 292.25 \(\pm\) 10.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The estimated turbidity of the extra concentration samples with the proposed method. It shows mean values \(\pm\) standard deviation of the turbidity estimated by the CNN and the MLR for each color dataset for paprika samples. which is high compared to measurements made with commercial turbidimeters, and prevailing algorithms of machine learning. * Once the CNN model is trained and the MLR is fitted, the algorithm can be used on a smartphone or other devices with a lower cost Nvidia GPU. In this research, the execution of the TSS and turbidity estimation algorithm was validated on a smartphone (Huawei Nova 3) using \"Pydroid 3\", and the same results were obtained according to the Google Colaboratory service. * As further work, a range extension (adding larger TSS values) and an expansion of the training dataset (more images) could be performed to achieve better method performance. Moreover, further research should also investigate heterogeneous material samples such as river water, domestic and industrial wastewater, potable water, and different sediments on the seabed, among others. In addition, it is expected that this research study will be helpful for developing a device for actual applications; for example, a mobile device where both the camera and the liquid sample are encapsulated, thus avoiding influence on the measurements by external conditions of light and background. Conceptualization, D.L.-B., I.M., and C.G.-M.; methodology, D.L.-B., I.M., and C.G.-M.; software, C.G.-M.; validation, T.S.-A. and E.G. ; formal analysis, D.L.-B. and C.G.-M.; investigation, D.L.-B. ; resources, C.B.-C. and J.G.-T.; data curation, D.L.-B. and C.G.-M.; writing--original draft preparation, D.L.-B. and I.M. ; writing--review and editing, D.L.-B., I.M., and C.G.-M.; visualization, T.S.-A. and E.G. ; supervision, I.M.