AI-Driven Crop Growth Prediction using Raspberry Pi
Objective
The main objective of this project is to develop an AI-Driven Crop Growth Prediction System using Raspberry Pi that uses sensors to monitor environmental parameters and predict crop growth through machine learning. It helps farmers make accurate decisions for better yield. The project focuses on creating a prototype that enables intelligent prediction and real-time monitoring for smart farming
Abstract
The AI-Driven Crop Growth Prediction using Raspberry Pi is an intelligent agriculture monitoring system designed to improve crop productivity through real-time environmental monitoring and image-based crop analysis. The system uses a Raspberry Pi as the main controller along with a USB camera for capturing crop images and detecting damaged leaves, diseases, or growth abnormalities using Artificial Intelligence techniques. A soil moisture sensor, pH sensor, and DHT11 temperature and humidity sensor continuously monitor soil and environmental conditions affecting crop growth. The collected data is analyzed to predict crop health and growth status. An LCD displays sensor readings and prediction results, while a buzzer generates alerts whenever abnormal conditions are detected. A relay-controlled DC water pump is automatically activated based on soil moisture levels and crop requirements. When crop damage or disease symptoms are identified through image analysis, the system provides timely alerts and initiates appropriate irrigation actions. The proposed system supports precision agriculture by enabling automated monitoring, early disease detection, efficient water management, and improved crop yield.
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
- SD Card
- LCD Display
- USB Camera
- Soil Moisture Sensor
- pH Sensor
- DHT11
- Relay Module
- DC Water Pump
- Buzzer
- Power Supply
- 12V Adapter
- Connectors – 30
Software components:
- Python
- Raspbian 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:
o Planning and Requirement Gathering (software’s, Tools, Hardware components, etc.,)
o Schematic preparation
o Code development and debugging
o Hardware development and debugging
o Development of the Project and Output testing
· Practical exposure to:
o Hardware and software tools.
o Solution providing for real time problems.
o Working with team/ individual.
o Work on Creative ideas.
· Project development Skills:
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o 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.
