PlantDX

📝 Overview

In Toba, where tourism is thriving, there are many hotels and restaurants, and a lot of vegetable oil is disposed. The city also has a thriving aqua culture industry, which discards many oyster shells as well. Therefore, a biodiesel fuel (BDF) production plant was built to recycle vegetable oil and oyster shells as BDF. However, there was a technical problem with the process of stirring the waste oil and methanol. It was impossible to visually see if the strring was going well. If the stirring was not done properly, the subsequent process would be affected. So we began research to develop an Image Analysis AI that classifies the achievement rate of agitation into five levels based on post agitation images and an IoT system to make the plant more manageable.

🔧 Usage

1. Clone the repository

git clone https://github.com/yushin-ito/plantDX.git

2. Go to the app directory

cd app

3. Install all the necessary dependencies

npm install

4. Start the dev server

npm run dev

✅ Todo

1. Capture images by TimerCameraF(ESP32) and send them to a web server
2. Set up a web server with M5Stamp(ESP32) to collect and send images and sensor data
3. Team development of Web Applications using Supabase and Next.js
4. Development of AI (CNN) to classify the achievement rate of agitation using Tensorflow

🌐 System

🚀 Features

• Web Application

  Developed UI to display and remotely control the status of BDF production plant.






• IoT Device

  Two IoT devices will be used to monitor and remotely operate the BDF production plant.






• Image Analysis AI

  The image of waste oil and methanol after stirring is used to classify the achievement rate of the stirring into 5 levels.

🛠️ Technology

• Next.js
• Next UI
• React-Hook-Form
• Supabase
• ESLint

👀 Author

• Yushin Ito
• Valtteri

📜 License

MIT License