# Promotion of Color Sorting in Industrial Systems Using a Deep Learning Algorithm
## 4 Conclusions  
Results of the work show that convolutional neural networks, specifically the YOLOv3 algorithm on the raspberry example, achieve accuracy and speed at a high level in detection, localization, and classification. In all 9 trainings, the model accuracy, according to F1 measure, ranged between 92 and 97%. The impact of hyperparameters defining the YOLOv3 model was assessed. Learning rate did not have significant influence on model performances. The training set size is assumed to be the reason. The number of epochs, trained anchor boxes, and the choice of batch size influenced performances and the classification precision of the model. It is also possible to obtain good results with default hyperparameters and anchor boxes. The effects of augmentation techniques on model performances are significant, according to the results. The augmentation technique that produced the greatest positive impact on the final defined model was blurring, while the rotation technique had a negative impact. There are two significant findings. The first one is that the detection and classification of an object such as a stalk represents one of the significant results of YOLOv3 algorithm application due to its shape, small dimensions, and variations. Detection, localization, and classification of noncompliant raspberries that had various types of damage and shape, and were classified into a single class, can be also considered an outstanding result of the YOLOv3 algorithm implementation. Since various results define how successful an algorithm is (e.g., precision, training time, speed detection, etc. ), the results are not influenced only by the choice of the algorithm type but also by the hardware. The reliable result and algorithm choice can be confirmed only by direct use on the machine. Authors agree that this method can be useful as a nondestructive method for detection, localization, and classification of agricultural products during machine inspection. Average detection time in full resolution images (1024 \(\times\) 1024) amounts only to 0.37 s with the selected middle-class software components. With a better graphics card and overall system, this time can be additionally reduced. The application of this method can largely promote the accuracy of object identification, accelerate the efficiency of agricultural products' sorting, increase objectivity in decision making (i.e., machine learning), and ensure greater economic benefits of the entire process. The paper also presents a developed model of software solution in the form of a graphical interface that allows for testing of the machine learning algorithms and potential application during real processing of agricultural products. Future development of the application functionality would include forwarding new images intended for training through online services, after which a new version of the weight factor could be sent to the application. The next direction of development of this topic would involve investigation of the unsupervised learning algorithm application, where the possibility of processing a larger amount of data in real time would be increased. In general, it is necessary to set out and create solutions that apply deep learning in such systems of industrial production. A general improvement in the new area of deep learning and convolutional neural networks is evident, and essential for further investigation and application in smart and sustainable agricultural production, as well as for achieving greater precision and safety in the food industry. Conceptualization, I.M. ; methodology, I.M. ; software, I.M. ; validation, I.M., J.I. ; formal analysis, I.M., E.V. and A.J. ; investigation, I.M. ; resources, I.M., E.V. and A.J. ; data curation, I.M., E.V. and A.J. ; writing--original draft preparation, I.M. ; writing--review and editing, I.M., E.V., A.J. and J.J.; visualization, I.M. ; supervision, J.I. All authors have read and agreed to the published version of the manuscript. This research received no external funding.