###### Abstract
Machine learning algorithms can then be applied to predict the quality and shelf-life of food products, allowing better storage management [18]. ML can also be applied to identify; remove blemishes and contaminants; and monitor and realize data flow in industrial production between different machines, i.e., their sensors and cloud processing. ML will be able to analyze a considerable amount of data generated and use the skills gained to apply them (online) to food products. Finally, ML algorithms can be applied to optimize production processes, allowing increased productivity and efficiency in the food industry. In the next step, ML, through the mentioned activities, implemented them in food processing (Industry 4.0). In the future, in cooperation between man and machine (Industry 5.0), ML will make it possible to direct processes in such a way as to anticipate every next step. It will efficiently assess the shelf life of each food product during and after its production. Thus, it will translate into reduced waste and lower the scale of food waste. The current smart reality offers solutions also using deep learning to recognize, identify, transcribe, optimize and predict the problems posed. The popularity of deep learning started to be noticeable at the beginning of the year 2010 [19]. Deep learning (DL) is defined as one of the subcategories of machine learning. The essence of the operation is the creation of artificial neural networks aimed at improving, for example, image recognition (commonly used in food, among other applications) or speech [20]. The process of learning is defined as deep learning because the structure of artificial neural networks consists of numerous inputs, outputs and hidden layers. Each layer also contains units that convert input data into information that subsequent layers can use to perform the prediction task. In this way, the machine learns by means of its own data processing. In the food industry, where deep learning is applied, it is of crucial importance to put special emphasis on subcategories responsible for image analysis and image recognition. Considering the seasonal nature of fruits and vegetables, all methods that are aimed at preserving food and prolonging its shelf life are of fundamental importance. In view of the above, more active efforts are being made to search for methods supporting the preservation of food, especially those rich in nutrients [5; 6]. It is worth knowing that changes in the metabolic transformation of vegetables and fruits are dependent on the action of high temperatures. Even room temperature affects the deterioration of vegetables and fruits by degradation of compounds, such as oxidation of vitamins, increased moisture and water activity leading to microbiological spoilage. When using the right method to fix the quality condition of vegetables and fruits, it is possible to eliminate their spoilage and the development of bacteria and other microorganisms [14; 15]. Controlling water content will help extend their shelf life during storage (especially for fruits harvested seasonally). Among those methods, the most important are the ones that allow the preservation of fruits and vegetables and guarantee that the obtained final products will have favorable forms. Drying allows for slowing down physiochemical changes, which translates into maintaining high-quality dried raw materials [2]. However, this process also sometimes triggers unfavorable chemical reactions such as the process of oxidization, non-enzymatic browning and vitamin transformation. It can also lead to structural changes (volume, porosity, density), sensory changes (taste, smell), textural changes, nutritional changes or visual changes (appearance, color). Nevertheless, drying has become very popular in the food industry, especially in the process of preserving food articles at the last stage of food production. Therefore, this process is often responsible for the final quality and properties of the product and can be shaped by an appropriate selection of parameters.