Language
Python
Tool Type
Algorithm
License
The MIT License
Version
1.0.0
Municipalidad de Vicente López
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Trampas Barceló uses IoT technology and artificial intelligence to efficiently detect and monitor Aedes aegypti mosquitoes. This system significantly reduces the need for manual inspections and resource usage, delivering accurate data in real time. Developed to improve efficiency in the detection and management of mosquito populations, this tool saves resources and improves environmental response, contributing to public health and environmental conservation by reducing the use of pesticides.
Barceló Traps optimizes the detection and management of mosquito populations, improving efficiency and precision in vector control and reducing the environmental impact of traditional control practices.
Utilizes IoT and AI for mosquito detection. Automates surveillance processes. Optimizes pest control operations. Enhances public health efforts.
Built on Python, it integrates IoT technologies to enhance connectivity and data collection. Employs artificial intelligence for advanced data analysis, optimizing accuracy and efficiency in results. Implements open standards to ensure interoperability between different systems and platforms. Distributed under the MIT license, it encourages collaboration and sharing through its open-source nature.
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Proposal for an IoT device with LED lights and camera to trap mosquitoes, identified by AI. Uses Raspberry Pi and remote monitoring. Focused on Aedes mosquitoes, saves resources by checking only when full.
Descriptive image of a text on AI in Vicente Lopez identifying mosquitoes, with a photo showing detection of "Aedes" and "Mosquito" through color boxes and accuracy percentage.
This image depicts a cloud-based infrastructure for a photo capture system, integrating AWS services like S3 and EC2, Firebase, and Hugging Face for AI processing.
Report on the design and implementation of IoT traps for monitoring the Aedes aegypti mosquito.