Abstract
Until recently data acquisition in integrated pest management (IPM) relied on manual collection of both pest and environmental data. Autonomous wireless sensor networks (WSN) are providing a way forward by reducing the need for manual offload and maintenance; however, there is still a significant gap in pest management using WSN with most applications failing to provide a low-cost, autonomous monitoring system that can operate in remote areas. In this study, we investigate the feasibility of implementing a reliable, fully independent, low-power WSN that will provide high-resolution, near-real-time input to a spatial decision support system (SDSS), capturing the small-scale heterogeneity needed for intelligent IPM. The WSN hosts a dual-uplink taking advantage of both satellite and terrestrial communication. A set of tests were conducted to assess metrics such as signal strength, data transmission and bandwidth of the SatCom module as well as mesh configuration, energetic autonomy, point to point communication and data loss of the WSN nodes. Finally, we demonstrate the SDSS output from two vector models forced by WSN data from a field site in Belgium. We believe that this system can be a cost-effective solution for intelligent IPM in remote areas where there is no reliable terrestrial connection.
Highlights
Until recently data acquisition in integrated pest management (IPM) relied on manual collection of both pest and environmental data
To secure reliable data feeding into IPM spatial decision support systems we have developed the PentaSense wireless sensor networks (WSN) framework, which, coupled with Earth Observation (EO) at different spatial scales feeds into numerical models
We described our experimental PentaSense WSN network set-up in a field site in Belgium and output generated by two models related to the climatic suitability for the establishment and meteorological suitability driving the inter-annual dynamics of the mosquito vector Ae. albopictus
Summary
Until recently data acquisition in integrated pest management (IPM) relied on manual collection of both pest and environmental data. We believe that this system can be a cost-effective solution for intelligent IPM in remote areas where there is no reliable terrestrial connection. Pest monitoring and control is becoming an increasingly important issue worldwide, with invasive pest species seizing the spotlight and adapting to new environments at an alarming rate This is supported by the impact of climate change turning previously unsuitable areas to current and future hotspots for invasive species and emerging infectious disease. A lot of the existing systems rely on the availability of a power grid and WiFi or a cellular communication network This is not a problem in urban areas pest management requires surveillance in remote areas where a local network and power supply are not present, and the dependency on terrestrial networks is untrustworthy
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