Lightweight object detection model for False Smut detection in rice leaf with Eigen-cam Interpretability
Objective
Lightweight object detection model for False Smut detection in rice leaf with Eigen-cam Interpretability
Abstract
This project presents a lightweight rice leaf disease detection system using Raspberry Pi, camera module, LCD display, buzzer, and the YOLOv8 deep learning model. The system captures real-time images of rice leaves and accurately detects False Smut disease. The detection results are displayed on the LCD screen, while the buzzer provides an alert when the disease is identified. Eigen-CAM interpretability is used to highlight the affected regions of the leaf, improving the transparency and reliability of the detection process. The proposed system offers a low-cost, portable, and efficient solution for early disease detection, helping farmers reduce crop losses and improve agricultural productivity.
Keywords: YOLOv8, False Smut Detection, Rice Leaf Disease, Raspberry Pi, Eigen-CAM.
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
- LCD
- Web camera
- Buzzer
- connectors-10
Software requirements:
- Raspbian OS
- Python IDLE
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.
