## 3 Application of the Proposed Method to the Fruit Variety Classification
### Results and Discussion
#### 3.4.3 Comparison of the Results  
The research was focused on the synergy of two approaches, the object detection method (in our case, YOLO V3) and the classifier of the full frame and ROIs. Therefore, the comparisons can relate to each method separately or the whole proposed method, which calculated the _CF_s of object classes. First, a comparison of YOLO V3's performance in relation to other tested methods is presented in Table 10. The YOLO accuracy did not directly influence the result of the system's end inference. The YOLO V3 method affected the relation between the size of the object in the image and the image size itself in the training set and testing set, which in turn affected the accuracy of the fruit identification method using ROIs (in our case, CNN). In addition, YOLO accuracy also affected the number of classified objects (number of ROIs), which in turn affected the accuracy of the certainty factor. In the research, all the methods were tested with the same training data, which consisted of 926 files with multiple objects. As can be seen, the YOLO V3 generated the highest number of apple class detections, which could be used as the learning ROI images for the classification network. The best average processing times were obtained using the MobileNetV2 + SSDLite and SSD Inception v2 configuration, but the number of detections was much lower comparing to other architectures. Figure 5: An example of classification using the proposed system: uncertain classification (based only on the original image). \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{\
\begin{tabular}{c} **Number of** \\ **Apples on** \\ **Images** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **YOLO V3—Average** \\ **Execution Time (ms)** \\ \end{tabular} } & \multicolumn{2}{c}{**9-Layer CNN for**} & \multicolumn{1}{c}{**9-Layer CNN for**} & \multicolumn{1}{c}{**Presented**} \\  & & **Original** & **ROIs—Average** & **Method—Average** \\  & & **Image—Average** & **Execution Time (ms)** & **Execution Time (ms)** \\ \hline
**(1)** & **(2)** & **(3)** & **(4)** & **(5) = (2) + (3) + (4)** \\ \hline
1 & 200.97 & 96.96 & 92.97 & 390.90 \\
2 & 209.90 & 116.96 & 146.95 & 473.82 \\
3 & 208.96 & 115.00 & 159.95 & 483.91 \\
4 & 204.96 & 100.97 & 167.96 & 473.89 \\
5 & 208.92 & 94.95 & 182.94 & 486.81 \\
6 & 199.91 & 85.97 & 188.90 & 474.79 \\
7 & 211.90 & 92.00 & 242.90 & 546.80 \\
8 & 195.96 & 108.96 & 281.88 & 586.81 \\
9 & 216.93 & 107.97 & 302.86 & 627.76 \\
10 & 201.91 & 78.00 & 329.86 & 609.77 \\
11 & 215.96 & 90.00 & 329.97 & 635.92 \\ \hline \end{tabular}
\end{table} 
Table 9: Execution time analysis for the proposed method. The second method used in our approach was the nine-layer CNN model. When we compared the proposed CNN with the CNN built based on [23], we had a slightly higher overall accuracy rate (99.78% for the proposed CNN, 99.53% for CNN built on the basis of [23]). In both research works, the same training, validation, and testing data with original images were analyzed. In the learning process of both models, the augmentation methods were used. This improved the ability of models to generalize. In particular, RGB normalization, horizontal flips, image zoom (0.2), and image shear (0.2) were used. In addition, we used a simpler CNN architecture; it contained one less convolution pooling layer; therefore, the average processing time for one image was slightly lower (98.89 ms for the proposed CNN; 125.79 ms for CNN built based on [23]). The accuracy of the proposed method can be compared to others by treating the final classification of the variety of fruit with the maximum value of _CF_ (_CF\({}_{max}\)_). In Table 11, we present the comparison of the proposed method also with the CNN built based on [23]. In both studies, the same training, validation, and testing data were analyzed. We found that the proposed method had a slightly higher accuracy rate. We compared the proposed method with the method based only on the nine-layer CNN (Table 1) learned and tested with the original image.