Bees Monitor With Predators Repellent # 1 - Introduction

Table of Contents

• Introduction
• Getting Started
• Edge Impulse
• Improving Edge Impulse Model
• Testing The Machine Learning Model With OpenMV
• Adding The Water Sprayer System
• Testing The Water Sprayer System
• IoT Ambient Monitoring System | Part1
• IoT Ambient Monitoring System | Part2
• Summary
  

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Hi everyone! Below I show you the idea of my project to protect bees from their invertebrate predators. For this, I will use Machine Learning, which is not so easy since I intend to have a high prediction.

Elevator Pitch

This system distinguishes between bees and its invertebrate predators (wasps and spiders), to drive them away.

Summary

Invertebrate predators of bees: Among the enemies of the domestic bee, there are also effective predators that hunt their workers to feed themselves or their babies. Among the invertebrate bee hunters, we have hornets, praying mantises, wasps, and spiders. In this system we will detect and drive away wasps and spiders.

Description

How does it work?

1. First, I will collect a enough database of images of bees, wasps, and spiders. Some I will get from database websites and others using the Arduino Pro Nicla Vision board.
2. Then I will create a project in EDGE IMPULSE which is a development platform for machine learning on edge devices, free for developers.
3. I will follow the documentation regarding the Arduino Nicla Vision at: https://docs.edgeimpulse.com/docs/development-platforms/officially-supported-mcu-targets/arduino-nicla-vision
4. I will create an AI model with EDGE IMPULSE to distinguish between bees, wasps and spiders; and I will upload this model to the Arduino Pro Nicla Vision Board.
5. When the system detects a bee, wasp or spider, it will notify the Arduino MKR WAN 1310 board.
6. Additionally, when the system detects a wasp or spider, then a spray will be activated to repel these insects. Apparently, any liquid with citrus fruits is repellent of these insects.
7. Finally, I will use the MKR WAN 1310 to update my project posted on element14 taking advantege of its improvements: Monitoring and Protection an Ecological Area

Hardware

• Arduino MKR WAN 1310

• Arduino Pro Nicla Vision Board

• Arduino NANO 33 IoT
• DS18B20 Temperature Sensor
• DHT22 Humidity Sensor
• MQ-135 Air Quality Sensor
• LCD Displays 16x2 and 16x4
• Servo

Software

• Arduino IDE;
• EDGE IMPULSE; and
• OpenMV - MicroPython

Schematic

Below I show you the schematic diagram of the first part of my project.

And finally, the schematic diagram of the second part of my project.

NOTE: I don't rule out making some minor changes during experimentation until I get the best results.