Tea Leaf Guard: An Enhanced Mobile App for Tea Leaf Disease Detection Using Ensemble Deep Learning Models
Objective
To design and develop a mobile-based tea leaf disease detection system using ensemble deep learning models for accurate classification. To analyze leaf images in real time and provide reliable disease identification, improving detection accuracy while supporting farmers in timely decision-making and sustainable crop management.
Abstract
This project presents Tea Leaf Guard: An Enhanced Mobile App for Tea Leaf Disease Detection Using Ensemble Deep Learning Models, aimed at improving crop health monitoring and disease detection in tea plantations. The system is built using a Raspberry Pi integrated with a USB camera, LCD display, memory card, and power supply. The camera captures real-time images of tea leaves, which are processed using YOLOv8-based deep learning models trained to detect and classify various tea leaf diseases.The system analyzes the captured images and identifies whether the leaf is healthy or affected by disease. The results are displayed on the LCD screen, providing instant feedback to the user. By using advanced deep learning techniques, the system ensures high accuracy and fast detection. This solution helps farmers take timely action, reduces crop loss, and improves productivity. It can be effectively used in smart agriculture and plant disease monitoring applications.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Hardware components:
Raspberry Pi
Memory Card
USB Camera
LCD Display
Power Supply
Adapter
Software components:
Python
Rasbian OS
Learning Outcomes
- Understand Raspberry Pi architecture and GPIO configuration
- Learn how to install and configure Raspbian OS and required Python libraries
- Interface analog sensors with Raspberry Pi using MCP3008 ADC
- Implement image classification using Artificial Neural Networks
- Develop real-time skin analysis using USB camera input
- Build automated health screening systems with display and alert features
- Integrate temperature and heartbeat monitoring in diagnostic systems
- Analyze and interpret classification output for healthcare applications
- About Project Development Life Cycle:
- Planning and Requirement Gathering (software’s, Tools, Hardware components, etc.,)
- Schematic preparation
- Code development and debugging
- Hardware development and debugging
- Development of the Project and Output testing
- Practical exposure to:
- Hardware and software tools.
- Solution providing for real time problems.
- Working with team/ individual.
- Work on Creative ideas.
- Project development Skills:
- Problem analyzing skills
- Problem solving skills
- Creativity and imaginary skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
