# Convolutional Neural Network for Measurement of Suspended Solids and Turbidity
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & **White** & **Red** & **Blue** & **Green** \\ \hline Accuracy & 1.00 & 1.00 & 1.00 & 1.00 \\ Precision & 1.00 & 1.00 & 1.00 & 1.00 \\ Recall & 1.00 & 1.00 & 1.00 & 1.00 \\ F-Score & 1.00 & 1.00 & 1.00 & 1.00 \\ Fish Feed training time (min) & 65.38 & 102.88 & 156.53 & 174.11 \\ Paprika training time (min) & 162.49 & 200.58 & 232.62 & 236.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance measures of the CNN as classifier for each color dataset. Figure 9: ROC curve obtained in the CNN performance evaluation (all colors). **Note:** all classes overlap in the horizontal line. -fish feed, whose spectrum has more red-light content, and then the red image may have more information, which may improve the red channel analysis. Regarding the green and blue channels, the wavelength peak of green LED illumination is about 40 nm displaced from the peak wavelength of the green pixel responsivity of the CMOS camera, while the wavelength peak of the blue LED lamp is about 10 nm displaced. This spectral mismatch may reduce the information of the green channel in comparison with blue and red channels. This is similar for paprika samples whose spectrum has more red-light content. MLR performance measures were calculated and the results obtained for the different color datasets are shown in Table 4. When looking at the results in Table 4, it is observed that the MLR for the white dataset had the highest value of the coefficient of determination \(R^{2}\), and furthermore, the highest model quality to replicate the results. In addition, the MLR with the white dataset had the lowest MAE and MSE values. Therefore, the best MLR performance for the estimation task was performed with the white dataset, unlike the green illuminated samples, which had the worst MLR performance. The study was mainly focused on fish feed and, to validate our results, paprika was tested. For both samples, the CNN + MLR have the performance listed in Table 4. The liquid samples created in this research have a TSS range of 0-800 mgL\({}^{-1}\). The TSS values, estimated by both the CNN + MLR, are shown in Table 5 for the different color datasets. Performance metrics shown in Tables 4 and 5 indicate that the best light illumination for the proposed method was white color for the fish feed sample. This is because the white color showed an error of 2.53% compared to 3.16% for red, 4.16% for blue, and 9.57% for green. The proposed method with the white dataset had an operational range \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Metrics** & **Sample** & **White** & **Red** & **Blue** & **Green** & **Sample** & **White** & **Red** & **Blue** & **Green** \\ \hline \(R^{2}\) & 0.991 & 0.983 & 0.958 & 0.913 & & 0.999 & 0.991 & 0.997 & 0.984 \\ MAE & 0.016 & 0.021 & 0.035 & 0.052 & & 0.006 & 0.016 & 0.009 & 0.030 \\ MSE & 0.0004 & 0.0009 & 0.0022 & 0.0045 & & 0.0002 & 0.0009 & 0.0004 & 0.0004 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MLR performance metrics for each color dataset for the fish feed and paprika samples. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Samples** & **Classes** & \multicolumn{4}{c}{**Colors**} \\ \cline{3-6}  & 0 & 0.019 \(\pm\) 0.024 & 0.021 \(\pm\) 0.002 & 0.021 \(\pm\) 0.035 & 0.083 \(\pm\) 0.102 \\  & 1 & 0.109 \(\pm\) 0.013 & 0.117 \(\pm\) 0.026 & 0.118 \(\pm\) 0.031 & 0.120 \(\pm\) 0.056 \\  & 2 & 0.189 \(\pm\) 0.014 & 0.203 \(\pm\) 0.016 & 0.215 \(\pmof 0 to 0.8 gL\({}^{-1}\) and high goodness of fit (\(R^{2}\) = 0.99). Therefore, the method with the white dataset obtained the lowest error of \(\pm\)2.53% and a general standard deviation of \(\pm\)0.018, which implies an accuracy of 97.46%. These errors are the measurement errors (observational errors), which were calculated by using the values of the reference samples listed in the Table 1 (taken as the true values). In general, the results indicate that the measuring precision is reasonably good for the fish feed sample.