## 5 Results and Discussion
### Descriptive Statistics  
In this section, descriptive statistical measures were used as predictors, such as the variance, the standard deviation, the mean, and the median, see Equation (7). Table 2 shows that diseases and defects can be easily discriminated against according to their average threshold value and bounds. Remember that the classification is based on a binary learning system between a healthy raspberry vs. a defective raspberry, as shown in Figure 8.  
Table 2 shows that the average threshold for a healthy raspberry is lower for all predictors, except for the standard deviation for the over-ripeness defect. While for the bounds, comparisons can be made even though there are some overlaps. Let us take the median predictor as an example. The median has an average threshold value of 72.04 for a healthy raspberry, which is lower than the rest of the average threshold values: 80.64 for albinism disease, 96.38 for fungus rust disease, 91.36 for over-ripeness defect, and 99.37 for peduncle defect. This suggests that each disease and each defect of a raspberry concerning a healthy raspberry can be discriminated by an average threshold. Similarly, it happens with the bounds. A healthy raspberry is between the values of \([56,169]\). Then the lower bound has a value of 56 and the upper bound has a value of 169. By comparing the lower bounds, we can observe that the 56 value is different for all predictors. A 28.50 value for albinism disease, a 27 value for fungus rust disease, a 59 value for over-ripeness defect, and a 28.5 value for peduncle defect. While for the upper bounds, we can observe that the value  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Albinism** & **Fungus-Rust** &
\begin{tabular}{c} **Over-** \\ **Ripeness** \\ \end{tabular} & **Peduncle** & **Healthy** \\ \hline \multirow{3}{*}{_Variances_} & 28,860.80 & 31,056.00 & 19,936.33 & 30,292.76 & 21,707.87 \\  & [70.92, 97,348.67] & [575.00, 86,887.3] & [1300.33, 55,216.00] & [67.58, 86,548.67] & [44,418.97] \\ \hline \multirow{3}{*}{_stds_} & 163.01 [8.42, 312.01] & 166.53 [23.98, 294.77] & 136.39 [36.06, 294.19] & 164.37 [12.95, 294.19] & 145.71 [65.11, 201.76] \\ \hline \multirow{3}{*}{_Means_} & 156.82 [39.00, 278.25] & 157.53 [60.25, 274.50] & 158.08 [86.00, 276.25] & 160.99 [32.75, 276.25] & 154.91 [86.00, 278.80] \\  & 278.25 [27.50] & 274.50 & 255.00 & 276.25] & 230.88 \\ \hline \multirow{3}{*}{_Medians_} & 80.64 [28.50, 96.38 [27.00, 91.36 [59.00, 99.37 [28.50, 72.04 [56.00, 242.50] & 233.00] & 241.00] & 169.00 \\  & 242.50 [23.00] & 203.00 & 241.00] & 169.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis using descriptive statistics. The table shows the average thresholds and the learning bounds of each class.  
of 169 is lower for all descriptors. A 242.50 value for albinism disease, a 233 value for fungus rust disease, a 203 value for over-ripeness defect, and a 241 value for peduncle defect. There is some overlap. In the same way, it happens with the other predictors. Applying this analysis of all the predictors together, we suggest that these differences between the lower and upper bounds can achieve good performance on the ML approach. Note that these predictors are the input for the neural network to classify and detect diseases and defects in raspberries, see Figure 8. Figure 11 shows the representation of the distribution of numerical data for each class studied. Observe the large similarity that all the figures have.