Summary of Mosquiot: a System Based on Iot and Machine Learning For the Monitoring Of Aedes Aegypti (diptera: Culicidae), by Javier Aira et al.
MosquIoT: A System Based on IoT and Machine Learning for the Monitoring of Aedes aegypti (Diptera: Culicidae)
by Javier Aira, Teresa Olivares Montes, Francisco M. Delicado, Darìo Vezzani
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an innovative system called MosquIoT, which combines traditional ovitraps with Internet of Things (IoT) and Tiny Machine Learning (TinyML) technologies. The goal is to detect and quantify the eggs of the Aedes aegypti mosquito species, which are responsible for spreading diseases like dengue, yellow fever, chikungunya, and Zika. The system aims to enable proactive and predictive entomological monitoring, shifting from traditional reactive methods. By leveraging IoT and TinyML, MosquIoT can provide real-time insights into Ae. aegypti populations in cities, supporting more informed decision-making and resource allocation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mosquitoes spread diseases that affect millions of people worldwide. To prevent this, we need better ways to track mosquito populations. This paper creates a new system called MosquIoT. It combines old methods with modern technology like the Internet of Things (IoT) and small machine learning (TinyML). MosquIoT looks for mosquito eggs in traps that can be connected to the internet. This helps us understand how mosquito populations change over time, allowing us to make better decisions about how to stop diseases. |
Keywords
* Artificial intelligence * Machine learning
