Artificial Intelligence in Agriculture: A Systematic Review of Crop Yield Prediction and Optimization
Objective
To systematically review and analyze existing Artificial Intelligence techniques used for crop yield prediction and agricultural optimization. To evaluate the performance, methodologies, challenges, and future research directions in AI-driven agricultural systems for improving productivity and sustainability
Abstract
The Artificial Intelligence in Agriculture: A Systematic Review of Crop Yield Prediction and Optimization system is designed to enhance crop management by leveraging AI-based predictive modeling and environmental monitoring. The proposed system integrates multiple sensors including a soil moisture sensor to assess soil quality, a pH sensor to monitor soil acidity, a DHT11 sensor for temperature and humidity measurement, and an MQ-135 sensor to evaluate air quality affecting crop growth. A web camera captures crop images to support visual analysis. A Raspberry Pi acts as the processing unit, collecting data from all sensors and executing a Random Forest-based Machine Learning model for crop yield prediction and optimization of resource utilization. Environmental parameters and predictive results are displayed on an LCD module for farmer awareness, while data can also be uploaded to IoT platforms for remote monitoring. This system facilitates informed agricultural decision-making, improving productivity, optimizing resource usage, and supporting sustainable smart farming practices through AI-driven analysis and predictive insights.
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Block Diagram
Specifications
Hardware components:
· Raspberry Pi
· Memory Card
· Power Supply
· Adapter
· Web Camera
· Soil Moisture Sensor
· pH Sensor
· DHT11 Sensor
· MQ-135 Sensor
· LCD Display
Software requirements:
· Raspbian OS
· Python
Learning Outcomes
Learning Outcomes
- Understanding Raspberry Pi architecture and pin configuration
- Installation and setup of Raspberry Pi OS
- Software installation and system configuration for Raspberry Pi
- Introduction to Raspberry Pi development environment
- Basic programming using Python for embedded applications
- Fundamentals of Embedded Systems programming
- Basics of IoT platforms and cloud connectivity
- Understanding power supply and hardware interfacing
- Knowledge of sensor interfacing with Raspberry Pi
Project Development Life Cycle
- Planning and Requirement Gathering (hardware, software, and tools)
- Circuit and schematic preparation
- Program development and debugging
- Hardware interfacing and troubleshooting
- System integration and output testing
Practical Exposure
- Working with hardware and software tools
- Developing solutions for practical monitoring systems
- Individual and team-based project implementation
- Implementation of innovative and creative ideas
Skills Developed
- Embedded system development
- Problem analysis
- Problem solving
- Programming skills
- Creativity and innovation
- System deployment
- Testing and validation
- Debugging techniques
- Project presentation
- Technical documentation and thesis writing
Paper Publishing
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