# Convolutional Neural Network for Measurement of Suspended Solids and Turbidity
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**Classes**} & \multirow{2}{*}{**TSS (gL\({}^{-1}\))**} & \multicolumn{2}{c}{**Turbidity**} & \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**TSS (gL\({}^{-1}\))**} & \multicolumn{1}{c}{**Turbidity**} \\  & & & **(NTU)** & & & **(NTU)** \\ \hline  & 0 & 0.000 & 0 \(\pm\) 0.55 & & 0.000 & 0 \(\pm\) 0.48 \\  & \({}^{1}\)/2 & - & - & & 0.002 & 2 \(\pm\) 2.45 \\  & \({}^{3}\)/4 & - & - & & 0.007 & 3 \(\pm\) 3.69 \\  & 1 & 0.100 & 38 \(\pm\) 2.61 & & 0.100 & 27 \(\pm\) 5.21 \\  & 2 & 0.200 & 77 \(\pm\) 7.06 & & 0.200 & 61 \(\pm\) 2.87 \\  & 3 & 0.300 & 95 \(\pm\) 13.62 & & 0.300 & 90 \(\pm\) 4.95 \\  & 4 & 0.400 & 132 \(\pm\) 12.37 & & 0.400 & 123 \(\pm\) 4.75 \\  & 5 & 0.500 & 168 \(\pm\) 12.67 & & 0.500 & 145 \(\pm\) 7.71 \\  & 6 & 0.600 & 189 \(\pm\) 22.66 & & 0.600 & 170 \(\pm\) 4.92 \\  & 7 & 0.700 & 245 \(\pm\) 21.65 & & 0.700 & 248 \(\pm\) 2.39 \\  & 8 & 0.800 & 263 \(\pm\) 23.73 & & 0.800 & 306 \(\pm\) 3.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Turbidity samples of the reference measured with the HACH DR900 for each TSS of the fish feed and paprika samples. Turbidity samples are shown as mean data \(\pm\) standard deviation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**Classes**} & \multicolumn{4}{c}{**Colors**} \\ \cline{3-6}  & & **White** & **Red** & **Blue** & **Green** \\ \hline  & 0 & 7.14 \(\pm\) 9.00 & 8.09 \(\pm\) 6.08 & 8.05 \(\pm\) 13.41 & 31.43 \(\pm\) 38.65 \\  & 1 & 41.39 \(\pm\) 4.94 & 44.70 \(\pm\) 10.03 & 44.98 \(\pm\) 11.6  
The results showed that the proposed method can be improved by including more classes with small concentrations in the CNN training. This can be noticed by comparing the test results for the fish feed and paprika samples. For example, with white light illumination, the measurement error is reduced from 2.53% to 1.76% for TSS estimation, and from 9.84% to 2.79% for turbidity estimation. These results provide evidence for the effectiveness of the proposed method, and indicate high resolution and accuracy. Nevertheless, despite the high performance this method offers, its associated limitations should be recognized. Among these are the type of samples that can be measured. The proposed method was tested with commercial fish feed and paprika as suspended solids, which do not represent all types of suspended solids. There are other aquatic environments such as river water, domestic and industrial wastewater, drinking water, among others, where the size of the particles may be smaller than that of the fish feed or paprika mass. However, as CNN analyzes the images in depth, with the high-level convolutional layers calculating all the differences between the images, their potential for application to other types of suspended solids is promising. This is because the CNN image analysis not only differentiates particle distribution, but also contrast, brightness, and color. This means that further research should explore this issue, considering other types of samples such as river water, domestic and industrial wastewater, and potable water, among others. In addition, this research is expected to become a prototype, in which we would have an encapsulation box with LED lighting included. In the box, we could place the sample and the smartphone in certain fixed positions and perform the measurement. In a future work, it could be tested if the LED flash (white light) of the smartphone could be used because the white dataset presented the best performance.