Green AI for Smart Agriculture: Energy-Efficient Predictive Models for Crop Yield and Resource Management
Objective
To design and implement energy-efficient Green AI–based predictive models for smart agriculture that accurately forecast crop yield and optimize resource management (water, energy, fertilizers) by minimizing computational power consumption while maintaining high prediction accuracy, thereby promoting sustainable and eco-friendly farming practices.
Abstract
Green AI for Smart Agriculture is an intelligent farming system designed to improve crop productivity while minimizing energy consumption and resource wastage. The system uses a Raspberry Pi as the main controller to monitor environmental and soil conditions in real time. Soil moisture, temperature, humidity, and pH sensors continuously collect field data, while a USB camera captures crop images for leaf disease detection using AI-based image processing techniques. The collected data is displayed on an LCD screen and uploaded to a cloud platform through IoT technology for remote monitoring. When soil moisture falls below the predefined threshold, a relay module automatically activates a DC water pump to irrigate the field. A buzzer provides alerts whenever abnormal environmental conditions are detected. Python is used for sensor data processing, disease detection, predictive analysis, and cloud data management. The proposed system enables efficient resource utilization, automated irrigation, early disease identification, and sustainable agricultural practices.
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
- Soil Moisture Sensor
- DHT11 Sensor
- pH Sensor
- USB Camera
- Relay Module
- DC Water Pump
- Buzzer
- Power Supply
- 12V Adapter
- Connectors – 30
Software components:
- Raspbian OS
- Python
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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