SILF Dataset: Fault Dataset for Solar Insecticidal Lamp Internet of Things Node.

Abstract

Solar insecticidal lamps (SILs) are commonly used agricultural pest control devices that attract pests through a lure lamp and eliminate them using a high-voltage metal mesh. When integrated with Internet of Things (IoT) technology, SIL systems can collect various types of data, e.g., pest kill counts, meteorological conditions, soil moisture levels, and equipment status. However, the proper functioning of SIL-IoT is a prerequisite for enabling these capabilities. Therefore, this paper introduces the component composition and fault analysis of SIL-IoT. By examining long-term operational data from seven nodes deployed in real-world scenarios, different fault modes are identified. Six typical machine methods are adopted to verify the validity of the proposed dataset. The results indicate that machine learning algorithms can achieve high accuracy on the proposed dataset. Notably, voltage, current, and meteorological data play a crucial role in the fault diagnosis process for both SIL-IoT and other related agricultural IoT devices.