## 3 Materials and Methods
### Maturity Status Estimator
#### 3.6.2 Fuzzy Logic  
Fuzzy Logic (FL) is a discipline of Artificial Intelligence that analyzes real-world information on a scale between true and false. This is integrated by three stages: fuzzification, the inference that uses a series of linguistic rules, and defuzzification. Figure 10 shows a structure of the fuzzy classification system proposed to classify bell peppers. It has four inputs and one output. The regions of interest of each shade correspond to the input and are used to identify the degree of maturity associated with the output fuzzy system. It is used to identify segments or regions of interest with shades of green, yellow, orange, and red. The output has different ranges for each class. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Inputs** & **Number of Neurons in the Hidden Layer** & **Output** & **Epochs** & **Accuracy** \\ \hline Model 1 & 4 & 4 & 4 & 10 & 92\% \\ Model 2 & 4 & 5 & 4 & 10 & 98\% \\ Model 3 & 4 & 8 & 4 & 10 & 100\% \\ Model 4 & 4 & 10 & 4 & 10 & 100\% \\ Model 5 & 4 & 15 & 4 & 10 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RBF-ANN maturity classifier. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & **Inputs** & \begin{tabular}{l} **Number of Neurons in** \\ **the Hidden Layer** \\ \end{tabular} & **Output** & **Epochs** & \
\begin{tabular}{l} **Mean Squared** \\ **Error (MSE)** \\ \end{tabular} &
\begin{tabular}{l} **Pearson Correlation** \\ **Coefficient (R)** \\ \end{tabular} \\ \hline Model 6 & 4 & 4 & 1 & 10 & 0.5483 & 0.68659 \\ \hline Model 7 & 4 & 5 & 1 & 10 & 0.5013 & 0.50130 \\ \hline Model 8 & 4 & 8 & 1 & 10 & 0.5016 & 0.73176 \\ \hline Model 9 & 4 & 10 & 1 & 10 & 0.4676 & 0.75910 \\ \hline Model 10 & 4 & 15 & 1 & 10 & 0.3888 & 0.79543 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Models using neural network Feedback prediction of Brix degrees. Figure 9: Architectural description of the model for \({}^{\circ}\) Brix prediction using Fitnet. Fuzzification of the input variables was carried out with membership functions of triangular type, which are characterized by their easy implementation in hardware. The linguistic variables used were low, medium, and high, as shown in Figures 11-14. The mathematical functions of each membership function are described in Equations (1)-(12). Together, the different pixel areas were used to increase the sensitivity of the fuzzy system to infer maturity changes. \[LowGARO=\{\frac{50-GAROI}{50}0<GAROI\leq 50\;050<GAROI\leq 100 \tag{1}\]  
\[MediumGAROI|\frac{GAROI}{50}0<GAROI\leq 50\;\frac{100-GAROI}{50}50<GAROI\leq 100 \tag{2}\]  
\[HighGAROI=\{00<GAROI\leq 50\;\frac{GAROI-50}{50}50<GAROI\leq 100 \tag{3}\]  
\[LowYAROI=\{\frac{29.09-YAROI}{29.09}0<YAROI\leq 16.48\;016.48<YAROI\leq 34.73 \tag{4}\]  
\[MediumYAROI=\{\frac{YAROI}{29.09}0<YAROI\leq 29.09\;\frac{58.17-YAROI}{29.08}V< YAROI\leq 58.17 \tag{5}\]  
\[HighYAROI=00<YAROI\leq 29.09\;\frac{YAROI-29.09}{29.09}29.09<YAROI\leq 58.17 \tag{6}\]  
\[LowOAROI=\{\frac{16.48-OAROI}{16.48}0<OAROI\leq 16.48\;\frac{OAROI-34.73}{18.25}16. 48<OAROI\leq 34.37 \tag{7}\]  
\[MediumOAROI=\{\frac{16.48-OAROI}{16.48}0<OAROI\leq 16.48\;\frac{OAROI-34.73}{18.25}16. 48<OAROI\leq 34.37 \tag{8}\]  
Figure 9:0  
Figure 9:1 Fuzzy maturity classifier. \[\text{Low }RROI=\left\{ \begin{array}{ll}\frac{16.48-RROI}{16.48}&0<RROI\leq 16.48 \\ \frac{RROI-34.73}{18.25}&16.48<RROI\leq 34.37\\ \end{array} \right. \tag{10}\] \[\text{Medium }RROI=\left\{ \begin{array}{ll}\frac{16.48-RROI}{16.48}&0<RROI \leq 16.48\\ \frac{RROI-34.73}{18.25}&16.48<RROI\leq 34.37\\ \end{array} \right.\] (11) \[\text{High }RROI=\left\{ \begin{array}{ll}\frac{16.48-RROI}{16.48}&0<RROI \leq 16.48\\ \frac{RROI-34.73}{18.25}&16.48<RROI\leq 34.37\\ \end{array} \right. \tag{12}\]  
Figure 11: Membership functions of GAROI input. Figure 12: Membership functions of YAROI input. Figure 15 shows the fuzzy Takagi-Sugeno model used for the development of the classifier; this was selected for its low computational cost unlike the Mandami model.