## 2 Related Work  
The VeggieVision system is one of the first classifiers of fruit and vegetable products [4]. It recognizes the product based on color and texture from color images according to a nearest-neighbor classifier. This system reports the most likely products to the user, one of which has a high probability of being the correct one. The accuracy of the system compared to currently achieved results is not too high: it is over 95%, however for the top four answers. The authors of [5] also used the nearest-neighbor classifier for fruit classification, but focused on the depth channel of RGBD (Red, Green, Blue, Depth) images. The use of hierarchical multi-feature classification and hybrid features made it possible to obtain better results for system accuracy among species of fruits, as well as their variety. The authors in [6] presented a vision-based online fruit and vegetable inspection system with detection and weighing measurement. As a preliminary proposal, the authors used an algorithm leveraging data on hue and morphology to identify bananas and apples. A fruit classifier based on additional extraction of color chromaticity was presented in [7]. A number of other studies [8] indicated that fruit recognition can be also provided by other classification methods such as fuzzy support vector machine, linear regression classifier, twin support vector machine, sparse autoencoder, classification tree, logistic regression, etc. The proposal to use neural networks as the fruit classifier was presented among others by [PERSON] et al. [9], who used a feedforward neural network. The authors first removed the image background with the split-and-merge algorithm, then the color, texture, and shape information was extracted to compose feature data. The authors analyzed numerous learning algorithms, and the FSCABC algorithm (Fitness-Scaled Chaotic Artificial Bee Colony algorithm) was reported to have the best classification accuracy (89.1%). Other applications of neural networks for fruit classification can be found in [10; 11]. In recent years, a number of articles have shown considerable modeling success with deep learning applications for image recognition. In [12], the authors applied deep learning with the Convolutional Neural Network (CNN) to vegetable object recognition with the results of learning rate being 99.14% and the recognition rate being 97.58%. In [13], the authors evaluated two CNN architectures (Inception and MobileNet) as classifiers of 10 different kinds of fruits or vegetables. They reported that MobileNet propagated images significantly faster with almost the same accuracy (top three accuracy of 97%). However, there were difficulties in predicting clementes and kiwis. This may be due to the choice of the training and testing of a variety of images, which were captured with a video camera attached to the proposed retail market systems and at the same time extracted from ImageNet. The article [14] presented a comparative study between Bag Of Features (BOF), Conventional Convolutional Neural Network (CNN), and AlexNet for fruit recognition. The results indicated that all three techniques had excellent recognition accuracy, but the CNN technique was the fastest at presenting a recognition prediction. In turn, in the article [15], two deep neural networks were proposed and tested for using simple and more demanding datasets, with very good results for fruit classification accuracy in both bases. Many numerical experiments for training various architectures of CNN to detect fruits were presented in [16]. A 13-layer CNN was proposed for a similar purpose in [17]. The literature review reported above was used to inform the use of computer vision techniques in an automated sales stand or self-checkout. The literature indicates that machine learning methods (especially CNN methods) perform well at classification of fruits and vegetables in the case of pre-prepared datasets.