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https://github.com/pranavsuriya-sr/agrosense
Smart Farming and Crop Management System
https://github.com/pranavsuriya-sr/agrosense
arduino-ide css esp8266 firebase html javascript kotlin machine-learning
Last synced: about 1 month ago
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Smart Farming and Crop Management System
- Host: GitHub
- URL: https://github.com/pranavsuriya-sr/agrosense
- Owner: pranavsuriya-sr
- Created: 2024-03-04T17:30:55.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-11-23T06:49:51.000Z (about 1 month ago)
- Last Synced: 2024-11-23T07:26:54.381Z (about 1 month ago)
- Topics: arduino-ide, css, esp8266, firebase, html, javascript, kotlin, machine-learning
- Language: HTML
- Homepage: https://agrosense-pranavsuriya.vercel.app
- Size: 5.32 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# AgroSense 🌾
**Smart Farming and Crop Management using IoT, ML, and Database Integration**
AgroSense is an innovative solution aimed at empowering farmers with real-time soil monitoring and crop management. By integrating IoT, machine learning, and a seamless multi-platform interface, AgroSense helps optimize farming decisions and boost crop yields.
---
## 🌟 Key Features
- **Real-Time Monitoring:**
Arduino-based soil sensors measure critical parameters like temperature, humidity, pressure, moisture, and pH levels.
- **Seamless Data Management:**
Sensor data is stored securely on **Firebase** for real-time access.
- **Mobile and Web Accessibility:**
Intuitive **Kotlin mobile app** and a **responsive web platform** for user interaction.
- **AI-Powered Insights:**
A **machine learning model hosted on Streamlit** provides actionable recommendations for optimal farming.
---
## 🚜 The Problem
Farmers often struggle with:
- Limited real-time data on soil conditions.
- Difficulty in making data-driven decisions for crop cultivation.
- Suboptimal yields due to inefficient soil and crop management practices.
---
## ✅ The Solution
AgroSense bridges the gap by providing a holistic solution:
- **Real-time sensor data** for informed decision-making.
- **Machine learning insights** for tailored crop management strategies.
- **User-friendly interfaces** for easy access to farming insights.
---
## 📷 Screenshots
**Sensor Monitoring Dashboard**
**Android Application**
---
## 🛠️ Tech Stack
### Hardware
- **ESP8266**: For interfacing soil sensors.
- **Sensors**: Temperature, humidity, pressure, moisture, and pH.
### Software
- **IoT Integration**: ESP8266 gathers and sends data to Firebase.
- **Database**: Firebase Realtime Database for secure and efficient data storage.
- **Mobile App**: Built with **Kotlin** for cross-platform compatibility.
- **Web Platform**: A responsive web app made with **HTML, CSS and JS** for desktop users.
- **Machine Learning**: Streamlit-hosted ML model for predictive insights.
---
## 🚀 How It Works
1. **Data Collection:**
Soil sensors connected to Arduino capture environmental data.
2. **Data Transmission:**
The Arduino sends data to Firebase in real-time.
3. **Data Analysis:**
The machine learning model hosted on Gradio analyzes the data to provide actionable insights.
4. **User Interaction:**
Farmers access the data and insights via the mobile app or web platform.
---
## Made With ❤️
AgroSense is crafted with love and dedication by [S R Pranav Suriya](https://github.com/pranavsuriya-sr).
Empowering farmers and promoting sustainable agriculture, one innovation at a time. 🌱