A Comprehensive Review of Deep Learning and Sensor-based Approaches for Efficient Mango Disease Detection
Objective
To analyze and review deep learning and sensor-based approaches for efficient mango disease detection in agricultural systems. To evaluate various machine learning models, datasets, and techniques for improving detection accuracy, while identifying challenges and future research directions for developing robust and real-time disease monitoring systems.
Abstract
This project presents a comprehensive review of deep learning and sensor-based approaches for efficient mango disease detection. The system is developed using a Raspberry Pi integrated with a USB web camera, LCD display, memory card, and power supply. The camera captures real-time images of mango fruits, which are processed using deep learning models to detect and classify various diseases.The system analyzes the captured images to identify whether the fruit is healthy or affected by specific diseases. The results are displayed on the LCD, providing instant feedback. This approach improves detection accuracy, enables early disease identification, and helps in reducing crop loss. The system can be effectively used in smart agriculture and fruit quality 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 Web 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.
