## 4 Conclusions  
This study proposed a double-track method for fruit variety classification in a retail sales system. The method used two nine-layer CNNs with the same architecture and different weight matrices. First, the network classified fruits based on images of fruits with a background and the second one \begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & \
\begin{tabular}{c} **Number of Apple Class** \\ **Detections** \\ \end{tabular} &
\begin{tabular}{c} **Average Processing Time for** \\ **1 Image (ms)** \\ \end{tabular} \\ \hline YOLO V3 [18] & 2735 & 182,17 \\ Fast RCNN Inception v2 [21] & 1168 & 96,15 \\ SSD Inception v2 [29] & 1006 & 46,61 \\ RCNN ResNet101 [30] & 2321 & 190,13 \\ MobileNetV2 + SSDLite [31] & 1265 & **42**,53 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of object detection methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Apple Variety** & **Number of Testing Data** & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \cline{3-4}  & & **DCNN Based on [23]** & **Proposed Method** \\ \hline Apple A & 104 & 98.08 & 99.04 \\ Apple B & 111 & 99.10 & 100.00 \\ Apple C & 151 & 100.00 & 99.34 \\ Apple D & 155 & 100.00 & 100.00 \\ Apple E & 100 & 100.00 & 100.00 \\ Apple F & 305 & 100.00 & 100.00 \\ Average/Total & 926 & 99.53 & 99.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of the two methods based on CNN. based on images with the ROI (a single fruit). The results were aggregated with proposed values of neuron weights (importance). Consequently, the method returned predicted class/classes (fruits variety/varieties) together with their Certainty Factor (_CF_). The presented method combined the detection and classification methods and determined the certainty factor associated with the prediction results from original and cropped images ROIs, which was the contribution of this paper. The _CF_s had an advantage in that the correctness of the classification result could be determined, resulting in more reliable predictions compared to the probabilities from the CNNs' outputs. This suggests that the proposed vision-based method can be used in uncertain conditions and unplanned situations as commonly encountered in sales systems (such as the accidental mixture of fresh products, placement of another object in the frame, unusual packaging of fruit, different lighting conditions, etc.). The test using 926 images of six apple varieties indicated that classification accuracy for this method (based on a maximal value of _CF_) was excellent (99.78%). In addition, the method was 100% successful at recognizing unambiguous, ambiguous, and uncertain classifications. It is important to recognize that the proposed method also had limitations. First, the method performed the classification process several times (for the whole image and detected objects), which could result in a longer time for obtaining the result. However, the uncomplicated structure of CNN and the YOLO V3 method for real-time processing [18] imply that the method can still be used in online sales systems. Second, the use of two different types of training images complicated the learning process of the system. Therefore, the learning process together with determining _CF_limit values in the proposed method is recommended for further research. In addition, the future direction is to test the method using a larger dataset containing greater amounts of different fruit and vegetable varieties of different species. It is also preferable to build a fruit and vegetable dataset with more demanding images, which will ultimately be the true test of the system. An interesting research direction will be testing the system with a dataset containing images of fruits and vegetables wrapped in a transparent plastic bag. This situation may cause uncertainty of the obtained result. Conceptualization, R.K. and M.P. ; formal analysis, R.K.; investigation, R.K. and M.P. ; methodology, R.K.; software, R.K. and M.P. ; supervision, R.K.; validation, R.K. and M.P.