Revolutionizing Agriculture with IoT and Machine Learning
DMeter
Introduction
Agriculture is the backbone of human sustenance and economic stability, yet traditional farming methods often face inefficiencies due to the lack of real-time monitoring. This project aims to bridge this gap by integrating Internet of Things (IoT) technology and Machine Learning (ML) into modern farming techniques. Our goal is to revolutionize agriculture by introducing intelligent, automated solutions that enhance productivity and sustainability.
Meet the Team
This project is the brainchild of an innovative and enthusiastic team from Parul Institute of Engineering and Technology, Bachelor of Technology (Information Technology).
Bhavin Parmar (2203031080042)
Jenish Paghadal (2203031080007)
Aryan Vekariya (2203031080125)
Yash Patel (2203031080015)
Rajdeep Dabhi (2203031080069)
The Challenge: Traditional Farming vs. Smart Farming
Traditional agriculture suffers from several inefficiencies:
Lack of real-time monitoring
Low crop production rates
Inefficient irrigation and resource management
High labor costs
Our Solution: IoT-Driven Smart Agriculture
We propose an IoT-based system that integrates real-time data monitoring, automated pest management, soil health analysis, and crop recommendations to optimize farming operations.
Our system will include:
✅ Real-time crop and soil monitoring
✅ Automated irrigation and pest control
✅ ML-based crop recommendation system
✅ Trespassing alerts using motion tracking
✅ Inventory management for fertilizers and seeds
Key Features and Benefits
Real-Time Crop Monitoring
Our embedded sensors continuously track soil moisture, temperature, and humidity. Farmers receive instant performance reports and alerts if abnormalities are detected.
Smart Crop Recommendation Model
Our ML-powered system analyzes soil data and suggests the best crops to grow, ensuring maximum yield and soil health.
Trespassing Detection & Security
24/7 infrared motion sensors detect unauthorized movement, alerting the farmer immediately.
Automated Irrigation System
Farmers can remotely control water pumps via our mobile-friendly platform, optimizing water usage.
Smart Inventory Management
A real-time inventory tracking system monitors fertilizer, seed, and resource levels, reducing waste and optimizing storage.
Technologies & Tools Used
Software Stack
Arduino IDE – Programming of sensors
Node, Express & React - Web Devlopment
Firebase – Database, hosting & backend services
Hardware Components
NodeMCU – IoT microcontroller
NPK Sensor – Measures Nitrogen, Phosphorus, Potassium levels
Temperature & Humidity Sensors – Monitors environmental conditions
Soil Moisture Sensor – Detects water content in soil
PIR Motion Sensor – Detects intruders
Implementation Process
IoT Device Setup
Sensors collect real-time data and transmit it via NodeMCU.
Data is sent to the Firebase database for processing.
Data Processing & Storage
Firebase handles data storage and retrieval.
The ML model processes the data and updates crop recommendations.
Web Interface
A user-friendly website displays real-time farm insights.
Farmers can monitor soil conditions, control irrigation, and view recommended crops.
ML Model Integration
The Crop Recommendation Model predicts suitable crops based on soil nutrients and past data.
It updates the database, which is reflected on the web platform.
Future Enhancements
We plan to extend our project with advanced features such as:
Crop Disease Detection – AI-based leaf image analysis for early disease identification.
Livestock Monitoring & Tracking – Using YOLO NAS AI for animal movement tracking.
Camera-Based Trespassing Detection – Replacing IR sensors with real-time object detection.
Revenue Model & Market Strategy
We aim to make IoT-based smart farming affordable for all farmers. Our strategy includes:
Prototype Testing with Agricultural Scientists & Institutions
Continuous Improvements Based on Expert Feedback
Launching a Market-Ready Product for Farmers
By integrating cost-effective solutions with cutting-edge technology, we plan to revolutionize the agricultural industry.
Challenges & Solutions
Potential Equipment Failures
- Implementing robust hardware and fault detection systems to minimize breakdowns.
Network Unavailability
- Using low-cost long-range communication protocols for uninterrupted connectivity.
Budget Constraints
- Seeking sponsorships, government grants, and crowdfunding to expand development.
Conclusion
By leveraging IoT and Machine Learning, our project aims to redefine modern farming, making it more efficient, productive, and accessible. This is just the beginning—our future enhancements will further empower farmers with AI-driven decision-making tools.
🔗 Stay tuned for updates as we continue to innovate in the AgriTech space!
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