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An IoT Smart Rodent Bait Station System Utilizing Computer Vision
Across the world billions of dollars of damage are attributed to rodents, resulting in them being classified collectively as the biggest animal pest in the world. At a commercial scale most pest control companies employ the labour intensive approach of deploying and manually monitoring rodenticide b...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506980/ https://www.ncbi.nlm.nih.gov/pubmed/32825006 http://dx.doi.org/10.3390/s20174670 |
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author | Ross, Robert Parsons, Lyle Thai, Ba Son Hall, Richard Kaushik, Meha |
author_facet | Ross, Robert Parsons, Lyle Thai, Ba Son Hall, Richard Kaushik, Meha |
author_sort | Ross, Robert |
collection | PubMed |
description | Across the world billions of dollars of damage are attributed to rodents, resulting in them being classified collectively as the biggest animal pest in the world. At a commercial scale most pest control companies employ the labour intensive approach of deploying and manually monitoring rodenticide bait stations. In this paper was present, RatSpy, a visual, low-power bait station monitoring system which wirelessly reports both on bait station levels and intruders entering the bait station. The smart bait stations report data back to a custom designed cloud platform. The system performance was evaluated under realistic field conditions (on an active cattle farm) with initial results showing significant potential in terms of reducing manual labour, improving scalability and data. |
format | Online Article Text |
id | pubmed-7506980 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75069802020-09-30 An IoT Smart Rodent Bait Station System Utilizing Computer Vision Ross, Robert Parsons, Lyle Thai, Ba Son Hall, Richard Kaushik, Meha Sensors (Basel) Article Across the world billions of dollars of damage are attributed to rodents, resulting in them being classified collectively as the biggest animal pest in the world. At a commercial scale most pest control companies employ the labour intensive approach of deploying and manually monitoring rodenticide bait stations. In this paper was present, RatSpy, a visual, low-power bait station monitoring system which wirelessly reports both on bait station levels and intruders entering the bait station. The smart bait stations report data back to a custom designed cloud platform. The system performance was evaluated under realistic field conditions (on an active cattle farm) with initial results showing significant potential in terms of reducing manual labour, improving scalability and data. MDPI 2020-08-19 /pmc/articles/PMC7506980/ /pubmed/32825006 http://dx.doi.org/10.3390/s20174670 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ross, Robert Parsons, Lyle Thai, Ba Son Hall, Richard Kaushik, Meha An IoT Smart Rodent Bait Station System Utilizing Computer Vision |
title | An IoT Smart Rodent Bait Station System Utilizing Computer Vision |
title_full | An IoT Smart Rodent Bait Station System Utilizing Computer Vision |
title_fullStr | An IoT Smart Rodent Bait Station System Utilizing Computer Vision |
title_full_unstemmed | An IoT Smart Rodent Bait Station System Utilizing Computer Vision |
title_short | An IoT Smart Rodent Bait Station System Utilizing Computer Vision |
title_sort | iot smart rodent bait station system utilizing computer vision |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506980/ https://www.ncbi.nlm.nih.gov/pubmed/32825006 http://dx.doi.org/10.3390/s20174670 |
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