Name
Abstract | ||
|---|---|---|
Outbreaks of forest pests cause large-scale damages, which lead to significant impact on the ecosystem as well as the forestry industry. Current methods of monitoring pest outbreaks involve field, aerial and remote sensing surveys. These methods only provide partial spatial coverage and can detect outbreaks only after they have substantially progressed across wide geographic areas. This paper presents an IoT system for real-time insect infestation detection using bioacoustic recognition via machine learning techniques. Specifically, we focus on detecting the Mountain Pine Beetle (MPB), which is the most destructive insect of mature pines in western North American forests. We present the design of the system and describe its various hardware and software components. Experimental results collected from a prototype implementation of the system are presented, which show that the system can detect MPB with 82% accuracy. We also demonstrate the applicability of our system in other noise monitoring applications, and report our experimental results on urban noise detection and classification. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/WF-IoT.2019.8767291 | 2019 IEEE 5th World Forum on Internet of Things (WF-IoT) |
Keywords | Field | DocType |
hardware components,mountain pine beetle monitoring,insect infestation detection,field surveys,aerial surveys,urban noise classification,urban noise detection,noise monitoring applications,software components,western North American forests,destructive insect,MPB,machine learning techniques,bioacoustic recognition,IoT system,wide geographic areas,remote sensing surveys,forestry industry,ecosystem,large-scale damages,forest pests | Environmental resource management,Computer security,Computer science,Internet of Things,Insect infestation,Component-based software engineering,Noise detection,Mountain pine beetle | Conference |
ISBN | Citations | PageRank |
978-1-5386-4981-7 | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edward M. Rochester | 1 | 0 | 0.34 |
| J. Ma | 2 | 126 | 16.41 |
| B. Lee | 3 | 0 | 0.34 |
| Majid Ghaderi | 4 | 271 | 31.77 |