A Low Computational Cost Deep Learning Approach for Localization and Classification of Diseases and Pests in Coffee Leaves
Objective
The main objectives of this project are to localize and classify diseases and pests in coffee leaves using a deep learning approach with low computational cost.It aims to detect multiple leaf conditions accurately while remaining efficient for deployment on resource-constrained devices. This solution supports early diagnosis and helps farmers take timely actions to protect coffee crops and maintain yield quality.
Block Diagram
Learning Outcomes
- Raspberry pi pin diagram and architecture
- How to install Raspberrypi / setup software
- Setting up and installation procedure for Raspberrypi
- Introduction to Raspberrypi environment / development setup
- Basic programming in Raspberrypi (Python)
- Basics of Embedded programming using Raspberrypi
- Basics of IoT platforms
- Working of power supply
- About Project Development Life Cycle:
- Planning and Requirement Gathering (software, 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
- Skills developed:
- Project development skills
- Problem analyzing skills
- Problem solving skills
- Creativity and imaginative skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
- Thesis writing 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.
