Automated Leaf Damage Assessment and Crop Classification Using Convolutional Neural Networks
Objective
To develop a portable and low-cost system using Raspberry Pi and CNNs for real-time crop classification and leaf damage assessment, aiding precision farming through early detection and efficient monitoring.
Abstract
This project presents an automated leaf damage assessment and crop classification system using CNN with Raspberry Pi and a web camera. The system captures real-time images of crop leaves and analyzes them to detect and calculate leaf damage percentage. Based on the detected damage level, it automatically classifies crop health and triggers a pesticide spraying system using a relay-controlled mechanism. This enables precise and timely pesticide application, reducing manual effort and supporting efficient crop management.
Keywords: CNN, Raspberry Pi, Leaf Damage Detection, Precision Agriculture, Crop Classification.
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 Web camera
- Buzzer
- Relay
- Lcd
- DC Water Pump
- Connectors-10
Software requirements:
- Raspbian os
- Python
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.
