# Promotion of Color Sorting in Industrial Systems Using a Deep Learning Algorithm
## 3 Results and Discussion
### Discussion  
The information and results provided above present the theoretical possibility of the YOLOv3 algorithm's real application in industrial production for the needs of agricultural products' sorting and inspection. As mentioned, the literature considered does not include works directly related to the subject of this research. To our knowledge, this is the first time that research employs real images from a color sorter, and we intended to initially contribute and open the theme for future research and application of deep learning algorithms in the industrial conditions of inspection. In [14; 32; 33; 43], only one or two classes were used as an output [44]. In relation to the more complex variations of the object of interest and the number of classes in our work, it can be concluded that the results obtained in this research are more significant because 5 classes are defined in our work and the results are promising: F1 as a measure of the proposed algorithm amounted to 92-97%, and the machine's current accuracy is 91-98%. Research study [16] defines multiple apple classes according to growth stages. Results of F1 measure indicated that the number of classes affects model output performances. On the other hand, the training set in this paper involves compliant raspberries with different ripeness levels, but they are defined as a single class, and there were no errors of this type on the test set. It should be noted that in their study, YOLOv3 is modified by the _DenseNet_ method, the aim of which is to better preserve and use significant characteristics with the YOLO network; therefore, this network produced better results in their research compared to the standard YOLOv3 network, whilst our work did not employ additional promotion of the network. The same group of researchers used the above-mentioned improved version of YOLOv3 to detect anthracose apples [13]. This study can represent the confirmation of the success of the YOLO algorithm in identifying infected fruit, as was carried out in this work, where only one disease is separated in three stages; however, our work is concerned with the examples of moldy raspberry, as well as different types of additional damages. In [43], the authors highlighted the advantages of YOLOv5 in terms of lightweight and better real-time performance. However, YOLOv3 produced exactly the same results as YOLOv5 for F1, mAP, recall, and precision. A result of approx. 100% would have been achieved in our work also, if the aim had been to detect raspberry only. Here, we wanted to formulate a realistic scenario of agricultural products' industrial inspection and to highlight multiple challenges of detection, e.g., stalk detection. In [45], the authors report that YOLO algorithms are continuously studied, and a table is shown containing the total number of created improved versions of different basic YOLO algorithms over a five-year period. Studies of the literature indicate small differences in precision results between YOLOv3 and later versions, but these are most evident in detection speed and the time needed for network training. In this context, if any other YOLO algorithm had been applied to our dataset, it would not have significantly affected our conclusions on potential applicability in industrial processing of agricultural products. Another reason for our belief that the application of CNN networks would produce good results in this field is that potential flaws of the application of networks such as CNNs include the fact that they do not encode the position and orientation of the object, and a great amount of training data is required, easily leading to a zero level with the use of augmentative methods such as rotation (or others), and to the easy processing of a large dataset in a short period of time. Furthermore, the industrial conditions of machine inspection are near-experimental: during product screening, in passing through a color sorter, the camera is always screening the product from the same distance, at the same angle, and the background does not change.