## 5 Conclusions
* A lot of research introduces an IoT-based shop floor material management system that can capture dynamic data in real-time and effectively synthesize it along the supply chain. The proposed system can lead to cost reduction, improved efficiency, and improved compliance with just-in-time (JIT) inventory management principles. * Due to the application of many new technology such as smart manufacturing, _artificial intelligence_, and RFID application have been presented the growing trend in past 3 years. 2. RFID applications in beehive model experiments: Until early 2000, direct observations and video recording of the foraging activity of social bees were the predominant techniques for studying bees' foraging behavior, and paint marks or labels were used to distinguish individuals. Although these techniques are still used, RFID technology has been used for bee monitoring and can automatically count the inbound and outbound movements of bees from the nest as well as recognize individuals. * Although these techniques are still used, RFID technology has been used for bee monitoring and can automatically count the inbound and outbound movements of bees from the nest and perform individual recognition. * In the literature over three years, new technologies have discovered a method for mining combined data from in-hive sensors, weather, and apiary inspections to forecast the health status of honey bee colonies. RFID applications in erratic tracking experimentation: These applications enable investigation of the interactions between wave activity and rocky coasts. RFID tags and localization systems have been employed to accurately measure the displacement of ocean sediments. Scholars have discovered that there are new trends applications in experimental environments such as monitoring low-energy high-temperature exposure 3D tags sensor, by deep learning principles as it outperforms traditional machine learning models to solve any tasks especially. 4. RFID applications in aquaculture: Convenient and automated systems for managing feeding on aquaculture farms are necessary to reduce costs and labor and for fish health management. In an RFID-based feeding system, a mass analyzer can identify the movement of feed and input the appropriate amount of feed into fish tanks. The input amount of feed is estimated by considering the change in weight detected by the mass analyzer. In addition, installing RFID readers, antennas, and tags is suggested for discriminating the feeding tank from a nearby tank. In the past three years, scholars have combined IoT and CIA technology to improve the performance of aquaculture operations. The researchers found that aquaculture is a potential emerging application, and the application is growing at present for the followings reason: * The advent of Cloud computing, Internet of Things, and Artificial Intelligence (CIA) has expanded numerous possibilities for applying and integrating information technology in all works of life. * In addition, the application of CIA in the aquaculture value chain to ensure effectiveness in traceability, feeding, disease detection, growth prediction, environmental monitoring, market information, and others is key to increasing aquaculture productivity and sustainability. * Thus, the future of aquaculture operations with less human labor, effective maintenance, and resource utilization largely depend on innovative technologies at present. The present study adopted the key-route main cluster path analysis in 3820 highly relevant papers of radio frequency identification to identify the paths of clusters and to develop their overall trajectories. In addition, relatively special applications were found in the clusters, i.e., radio frequency identification applications in privacy and safety, both databases (Scopus and Web of Science) recommend scholars study the direction of radio frequency identification technology applications in followings:  
1.