Leaf Disease Detection System using Raspberry Pi and Camera

Leaf Disease Detection System using Raspberry Pi and Camera

The Leaf Disease Detection System using Raspberry Pi and Camera offers an intelligent and affordable solution for early-stage identification of plant leaf diseases. By combining image processing with deep learning, the system accurately detects and classifies a variety of crop diseases.

At the heart of this system lies the Raspberry Pi, which integrates with a camera module, an I2C-based 16×2 LCD, and a Tkinter-powered GUI. Notably, the system uses a pre-trained Convolutional Neural Network (CNN) model (PLANT_MODEL.hdf5), capable of identifying over 35 plant diseases. These include conditions like blight, mildew, bacterial spots, and mosaic viruses affecting crops such as tomatoes, grapes, corn, and apples.

Users can either upload an existing image or capture a live one through the connected USB camera. Once the image is selected, the system preprocesses it and feeds it into the model. As a result, the CNN model returns a prediction along with a confidence score. Immediately afterward, the disease name appears both on the GUI and on the LCD screen for quick reference.

Unlike traditional manual inspection methods, this system ensures faster diagnosis and more consistent results. Furthermore, the interface remains user-friendly, allowing farmers or agricultural workers to operate it without technical expertise.

By using affordable hardware and open-source libraries, the system proves highly scalable. Therefore, it serves as an excellent tool for field deployment, research laboratories, and agricultural education.

Overall, this project showcases the potential of artificial intelligence in transforming agriculture. With further enhancements, it can even integrate cloud reporting or automated treatment suggestions, further benefiting the farming ecosystem.

Key Features

• Real-time image capture and disease analysis

• GUI-based interface with Tkinter

• Deep learning-based classification using CNN

• LCD display output via I2C

• Supports 35+ plant disease categories

• High accuracy predictions with confidence score

Applications

• Smart agriculture and precision farming

• Early detection of plant diseases

• Agricultural research and analysis

• Educational and demo tool for agritech

Hardware Used

• Raspberry Pi (any model with camera support)

• USB Camera / Pi Camera

• I2C 16×2 LCD Display (address: 0x27)

• Jumper wires and breadboard

• Power supply (5V)

• SD Card with OS

• Display monitor/HDMI for GUI

• Optional: Buttons for automation

To Learn More Visit our Website

For more information:-www.mifraelectronics.com 

* Product Images are shown for illustrative purposes only and may differ from actual product.