Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/kaymen99/agrigo
AI app for crop disease detection and fertilizer recommendation
https://github.com/kaymen99/agrigo
deep-learning flask machine-learning python3 tensorflow2
Last synced: about 2 months ago
JSON representation
AI app for crop disease detection and fertilizer recommendation
- Host: GitHub
- URL: https://github.com/kaymen99/agrigo
- Owner: kaymen99
- License: mit
- Created: 2022-03-24T14:06:21.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-11-21T14:03:54.000Z (about 2 months ago)
- Last Synced: 2024-11-21T15:19:25.040Z (about 2 months ago)
- Topics: deep-learning, flask, machine-learning, python3, tensorflow2
- Language: HTML
- Homepage:
- Size: 12.7 MB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AgriGo
---
## Introduction
Agriculture faces a host of challenges, from unpredictable weather conditions to soil degradation and plant diseases. These issues can reduce crop yields, increase costs, and impact food security. **AgriGo** is a web application designed to bridge the gap between modern agricultural practices and advanced technologies like machine learning and deep learning. By providing tools for crop disease detection, fertilizer recommendations, and crop selection advice, AgriGo empowers farmers to make data-driven decisions and optimize their farming processes.
---
## The Problem
Farming is becoming increasingly complex due to:
- **Limited access to expert advice** for small-scale farmers.
- **Inefficiency in crop selection** based on soil and environmental conditions.
- **Lack of knowledge about fertilizers** to use for specific soil and crop types.
- **Crop diseases going undetected**, leading to reduced productivity.
AgriGo addresses these challenges with an easy-to-use platform that integrates scientific analysis into daily agricultural practices.
---
## Features
### 1. Crop Recommendation
AgriGo analyzes soil properties like nitrogen, phosphorus, potassium (NPK) levels, moisture, temperature, and rainfall to suggest the most suitable crops for your farm. This ensures optimized crop selection tailored to your unique environmental conditions.
### 2. Fertilizer Suggestions
Using data such as soil type, pH, temperature, and the selected crop, AgriGo provides precise fertilizer recommendations. These suggestions help maintain soil health, improve crop growth, and maximize overall yield efficiency.
### 3. Crop Disease Detection
With just an uploaded image of your crop, AgriGo’s AI-powered image recognition system identifies diseases and evaluates plant health. This allows for quick interventions to protect your crops and prevent widespread damage.
---
## How to Use
### Clone the Repository
```bash
git clone https://github.com/kaymen99/AgriGo.git
cd AgriGo
```
### Run Locally with Python (v3.8)
1. **Create and activate a virtual environment**:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
2. **Install dependencies**:
```bash
pip install -r requirements.txt
```
3. **Start the server**:
```bash
python app.py
```
Visit the app at [http://localhost:5000](http://localhost:5000).
---
### Run with Docker
1. **Build the Docker image**:
```bash
docker build -t agrigo-webapp .
```
2. **Run the container**:
```bash
docker run -p 5000:5000 agrigo-webapp
```
Visit the app at [http://localhost:5000](http://localhost:5000).
---
## Dataset
The datasets used for this project are sourced from Kaggle:
- [Crop Recommendation Dataset](https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset)
- [Fertilizer Recommendation Dataset](https://www.kaggle.com/datasets/gdabhishek/fertilizer-prediction)
- [Crop Disease Image Dataset](https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset)
---
## Built With
- [Flask](https://flask.palletsprojects.com/en/2.0.x/)
- [TensorFlow](https://www.tensorflow.org)
- [scikit-learn](https://scikit-learn.org/stable/)
- [Bootstrap](https://getbootstrap.com/docs/5.0/getting-started/introduction/)
---
## Contact
For questions or support, please contact me: [[email protected]](mailto:[email protected])
---
## License
Distributed under the MIT License. See `LICENSE.txt` for more information.