This collection of project tutorials explores the potential of AI technology in detecting and monitoring. From environmental monitoring and pipeline diagnostics to smart grocery carts and food irradiation detection, these projects showcase how AI (Artificial Intelligence) and IoT (Internet of Things) can work together to create innovative solutions. Each tutorial provides a step-by-step guide, enabling you to harness the power of AI and IoT in your own projects.
Project 1. AI-driven LoRaWAN Fertilizer Pollution Detector w/ WhatsApp
Introduction: The tutorial explores the historical and contemporary use of fertilizers in agriculture, focusing on the balance between organic and chemical types. It delves into the environmental and health impacts of these substances, including soil contamination, water pollution, and associated health risks such as cellular degeneration and disease. The tutorial emphasizes the need for careful management of fertilizer use to sustain crop production while minimizing harmful effects.
Project 2. AI-assisted Pipeline Diagnostics and Inspection w/ mmWave
Introduction: The project provides a comprehensive guide to pipeline system maintenance, emphasizing the importance of efficient diagnostics in preventing costly repairs and replacements. It discusses common pipeline defects such as c/racks and corrosion, and explores various inspection methods, including computer vision, magnetic field measurements, and acoustic detection.
Project 3. AI-assisted Air Quality Monitor w/ IoT Surveillance
Introduction: This article addresses the critical issue of air pollution, focusing on the detection of hazardous gases such as nitrogen dioxide and ozone. It discusses the health and environmental impacts of these pollutants, and the importance of monitoring their levels for public safety. The tutorial proposes the creation of a cost-effective air station that uses machine learning to predict pollution levels and provides surveillance footage for further analysis.
Project 4. Pipeline Clog Detection with a Flowmeter and TinyML
Introduction: This project addresses the issue of pipeline clogs in industrial operations, discussing their causes, effects, and potential solutions. The tutorial introduces an innovative solution using artificial intelligence and machine learning to detect clogs based on flow rate sensor data. It outlines the implementation of this system using the Seeed Wio Terminal development board and a DFRobot Water Flow sensor, providing a comprehensive guide to setting up and using this technology.
Project 5. IoT AI-assisted Deep Algae Bloom Detector w/ Blues Wireless
Introduction: This tutorial addresses the issue of deep algal bloom in water bodies, discussing its causes, effects, and potential solutions.The tutorial proposes the creation of a cost-effective prewarning system that uses object detection to predict potential deep algal blooms. It outlines the implementation of this system using a borescope camera, Raspberry Pi 4, Notecard, and Edge Impulse, providing a comprehensive guide to setting up and using this technology.
Project 6. IoT AI-driven Smart Grocery Cart w/ Edge Impulse
Introduction: The tutorial presents a cost-effective solution for transforming regular grocery carts into smart grocery carts, enhancing the shopping experience. It discusses the use of machine vision applications, specifically the OpenMV Cam H7, to capture product images and train an object detection model. The tutorial also introduces the use of Edge Impulse's FOMO algorithm for model training, and outlines the process of deploying the model on the OpenMV Cam H7.
Project 7. IoT AI-driven Yogurt Processing & Texture Prediction w/ Blynk
Introduction: This tutorial provides a comprehensive guide to organic yogurt production, focusing on the precise preparation of prerequisites for fermentation. The tutorial introduces an innovative solution using an artificial neural network model to predict yogurt texture levels before fermentation. It outlines the implementation of this system using the XIAO ESP32C3 development board and various sensors, providing a cost-effective tool for improving product quality and reducing costs.
Project 8. AI-driven Forest Fire Prevention Robot w/ SMS & EZ-Robot
Introduction: This project presents a novel approach to mitigating forest fire risks by building a remote-controlled robot capable of detecting and removing potential fire hazards. The robot, constructed using EZ-Robot parts, is equipped with object detection and multi-color detection capabilities to track and remove discarded cigarette butts. It also features a temperature sensor and fan motor to detect and extinguish small burning debris. Additionally, the robot can notify users of incident locations via SMS, providing a proactive solution to forest fire prevention.
Project 9. IoT AI-driven Tree Disease Identifier w/ Edge Impulse & MMS
Introduction: The project introduces a cost-effective solution using the SenseCAP K1100 kit, which includes a Vision AI module and Wio Terminal, to capture images and environmental data. It outlines the process of training an object detection model with Edge Impulse and deploying it on a LattePanda 3 Delta.
Project 10. IoT AI-driven Poultry Feeder and Egg Tracker w/ WhatsApp
Introduction: This tutorial provides a comprehensive guide to automating the tracking of poultry feeder status and egg production using object detection. It discusses the importance of regular feeding schedules and monitoring egg production for maintaining poultry health. The tutorial introduces a cost-effective solution using the OpenMV Cam H7 and Edge Impulse's FOMO algorithm to recognize eggs and track feeder status. It outlines the process of training the object detection model and deploying it on the OpenMV Cam H7, providing a practical tool for small businesses and individuals raising poultry.
Project 11. IoT AI-driven Food Irradiation Dose Detector w/ Edge Impulse
Introduction: The project provides a comprehensive guide to predicting food irradiation doses using artificial neural networks. It discusses the importance of food irradiation for preserving food and the need for strict regulation to avoid health risks. The tutorial introduces a cost-effective solution using ionizing radiation, weight, and visible light measurements to predict irradiation dose levels. It outlines the process of training the neural network model and applying it to food samples exposed to natural radiation sources, providing a practical tool for small businesses to ensure compliance with food irradiation regulations.
Project 12. Detect harmful gases with Arduino and Machine Learning (Bhopal 84)
Introduction: This tutorial provides a comprehensive guide to building an AI-driven IoT device for detecting chemical leaks in industrial settings. It discusses the use of the DFRobot MiCS-4514 multi gas sensor and machine learning to identify subtle relationships between gas readings. The tutorial outlines the process of data acquisition, model training, and deployment on an Arduino BLE 33 Sense. It also includes instructions for 3D printing parts and setting up the device.
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