As the current agricultural industry is facing several challenges, including climate changes and lack of qualified farmers, it is extremely necessary to ensure sustainable agriculture and food supply by smart farming (SF). This can assist farmers and associated stakeholders in making correct decision that improves the yield and quality of agricultural product. Thus, an SF system based on low-cost Raspberry Pi and Internet of Things (IoT) technologies has been successfully implemented. Through the data items detected by sensors to deeply manage the planting process, the IoT-Enabled communication protocol under ISO standards of Message Queuing Telemetry Transport (MQTT) was used. The bar charts of real-time environmental parameters are presented on ThingsBoard to achieve the goal of data visualization. Meanwhile, a web server is built for the customized requirements, and the historical datasets are stored in the SQLite database system. Furthermore, a Petri net (PN) model is employed to detect all possible abnormal processes and to verify the feasibility and soundness of IoT-Enabled system by using a software tool, WoPeD. Finally, the experimental results show that the plants cultivated by the proposed IoT-Enabled system are superior to those cultivated by the traditional methods in many perspectives, especially a promising precision, 93.6%.

Correct use of home appliances is intended to avoid property damage and life unsafety. Also, serious fire accidents must affect the safety of one’s neighborhood. Therefore, this study uses the Internet of Things (IoT) platform to build an intelligent system that aims to measure the temperature rise of home appliances, which is employed to monitor the usage states of home appliances in real time. Hereby, the microcontroller, Node Micro Control Unit (NodeMCU-32S), is used to develop an IoT platform combined with sensors for measuring AC voltage, current, and ambient temperature so that the electrical power and temperature rise of load can be detected. Meanwhile, the smart measurement system is integrated with mobile devices to upload the detected datasets to the Google cloud database system. Thus, it can be used as samples for future calibration of system parameters with machine learning. Moreover, the verification of feasibility and soundness of a system model is performed by using Petri net tool, WoPeD, for the purpose of eliminating the improper states to optimize the system performance. Finally, the experimental results show that the proposed IoT-Enabled system has promising precision, 94.17％, and recall, 92.26％, which obviously outperforms other existing systems.