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LoRaWAN Based TinyML Snake Recognition System

A snake recognition project using XIAO ESP32-S3 Sense development board, combined with Arduino and Edge Impulse.

One truth is that snakes often appear in farms and pose a threat to human safety. I looked up some relevant information, according to United Nations Office for Disaster Risk Reduction, snake bites are a neglected public health issue in many countries, particularly in Africa, Asia, and Latin America. In Asia, up to 2 million people are envenomed by snakes each year, while in Africa, there are an estimated 435, 000 to 580, 000 snake bites annually requiring treatment. Moreover, bites from venomous snakes can lead to acute medical emergencies involving severe paralysis, respiratory problems, and bleeding disorders.

Therefore, I intend to develop a prototype project for automatically monitoring snakes in farm field environments to warn people that there are snakes here. To achieve this, we can utilize tiny machine learning for automatic recognition. However, we face the challenge of the remote and expansive nature of farm fields, making it impractical to rely on Wi-Fi or cellular networks for communication. After careful consideration, I have opted for LPWAN technology, specifically LoRaWAN. LoRaWAN provides a coverage range of several kilometers and ensures low power consumption, eliminating the need for frequent battery replacements.As a result, we have successfully created the LoRaWAN Based TinyML Snake Recognition System project, which you are currently witnessing.

Basic Thought
1. To ensure convenient outdoor deployment of the equipment while keeping costs relatively low, it is crucial to have a compact-sized solution. Additionally, the device should possess sufficient computing power to handle image recognition tasks. Considering these factors, I have selected the XIAO-ESP32-S3 Sense as the primary controller for this project. The XIAO-ESP32-S3 Sense is responsible for capturing images and feeding them into the model for accurate image detection and prediction.

2. For achieving long-distance transmission and minimizing power consumption, the Grove Wio-E5 has been chosen as the ideal solution for data transmission to the Things Network via LoRaWAN.

3. Moreover, I intend to directly monitor the snake detection status of the device. To facilitate this, I will visualize the data on Datacake, enabling easy interpretation of information through graphical representation. This visualization will provide a convenient way to check if the device has successfully detected snakes.”

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