This article presents a new model of customized mass production management with Industry 4.0 standards within the food industry. The aim of this article is to develop a method for managing the production line where it is possible to produce an entire spectrum of products without reconfiguring the production line. An illustrative example is the production of fruit yoghurt of various types. The entire life cycle of the product is monitored and documentation of all relevant raw material data is carried out through the production process all the way to product packaging where each product is specifically marked with QR code. A special technique for deciding on optimum maintenance of the production line has been introduced and a multi-criteria decision model has been developed using the fuzzy analytic hierarchy process method where it is possible to achieve a high degree of minimization of maintenance costs. In this work, a fuzzy-based multi-criteria decision making methodology is developed for conceptual design evaluation in the cost reduction in maintenance of mass customization process. For the purposes of monitoring the production process itself, a LabVIEW application was created in the form of a SCADA system.

This paper investigates a greenhouse microclimatic environment controlled by a mobile measuring station with the aim of improving performance by using wireless sensor networks (WSN) technology. The algorithms for the mobile measuring station that perform navigation tasks are called Bug algorithms. The existing potential field method based algorithms are improved with an RSSI signal propagation model and implemented on a two-wheel driven robot developing system and their performances are measured and analyzed. The implementation part is done on Boe-Bot equipped with SunSPOT enabling the wireless control ability. The control surface is generally made with LabVIEW and a relational database. Control strategy selection system is supported by Fuzzy Analytic Hierarchy Process (FAHP). Navigation of the mobile measuring robot can be done manually, relying on the visual data from the robot's camera, or it can be switched to automatic mode where the developed algorithm does the navigation job. Mobile robot navigation is based on the potential field method considering a wide range of energy-aware parameters

This paper focuses on the implementation of Industry 4.0-based maintenance approaches using AR technology. The primary objective is to highlight the benefits of AR in maintenance and provide a framework for implementing AR-based maintenance systems. AR can assist maintenance personnel in performing maintenance tasks more efficiently by providing real-time instructions and information. By overlaying virtual information on real equipment, AR can guide maintenance personnel through maintenance procedures, reducing human errors and improving the quality of the work. AR can also provide remote assistance to maintenance personnel, allowing experts to remotely guide less experienced personnel through complex maintenance procedures. The proposed application provides motorcycle service assistance using AR and telepresence technologies. It enables remote monitoring of the service flow and performs an automated identification of parts. The system provides instructions for disassembling more complex mechanical components, provides a 3D model of the object, and enables ordering of the necessary parts.

The paper briefly describes the specifics of precision agriculture from the point of view of data needs and the possibility of providing such data. It points to the most convenient and efficient form of collecting the necessary data using drones. Specific drone characteristics are recorded/discussed in order to facilitate selecting the right one according to the farmers' heterogeneous requirements regarding the data collection on their crops. Selecting the appropriate drone for the specific needs of farmers is carried out by a multi-criteria decision-making software.

This article presents a study on the fuzzy control of self-balancing, two-wheel-driven, simultaneous localization and mapping (SLAM)-based, unmanned systems for Agriculture 4.0 applications. The background highlights the need for precise and efficient navigation of unmanned vehicles in the field of agriculture. The purpose of this study is to develop a fuzzy control system that can enable self-balancing and accurate movement of unmanned vehicles in various terrains. The methods employed in this study include the design of a fuzzy control system and its implementation in a self-balancing, two-wheel-driven, SLAM-based, unmanned system. The main findings of the study show that the proposed fuzzy control system is effective in achieving accurate and stable movement of the unmanned system. The conclusions drawn from the study indicate that the use of fuzzy control systems can enhance the performance of unmanned systems in Agriculture 4.0 applications by enabling precise and efficient navigation. This study has significant implications for the development of autonomous agricultural systems, which can greatly improve efficiency and productivity in the agricultural sector. Fuzzy control was chosen due to its ability to handle uncertainty and imprecision in real-world applications.