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https://github.com/vishant007/annadataa
A Website For Farmers To Guide Them Regarding Crop Prouction In Their Native Language
https://github.com/vishant007/annadataa
django flask-application google-collab kaggle machine-learning-algorithms numpy pandas python3 scikit-learn
Last synced: 6 days ago
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A Website For Farmers To Guide Them Regarding Crop Prouction In Their Native Language
- Host: GitHub
- URL: https://github.com/vishant007/annadataa
- Owner: vishant007
- Created: 2023-07-25T14:46:53.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-19T08:30:09.000Z (10 months ago)
- Last Synced: 2024-04-28T00:24:29.666Z (7 months ago)
- Topics: django, flask-application, google-collab, kaggle, machine-learning-algorithms, numpy, pandas, python3, scikit-learn
- Language: HTML
- Homepage:
- Size: 1.73 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## Introduction:
- The primary objective of this project is to provide farmers with easily comprehensible knowledge in their native language about crops, ngo and government schemes regarding farmers.
- The HomePage, NGOPage,India's Map View Of Crops which are grown mostly in each state and Crop-Recommendation System on our website allow users to access our content in their local tongue. There is also a language change option available on every page.
## Tech-Stack:
- HTML/CSS, Python-Django, Flask, ML Algorithms, scikit-learn, numpy, pandas, kaggle
## Logic:
- The Crops-Recommendation-System is a Python-based project built on the Flask framework. It applies Machine Learning to determine the best crop to be grown in a specific condition.
- The system uses a trained model (`model.pkl`) based on crop recommendation datasets retrieved from Kaggle.
- The system takes into user inputs for Nitrogen, Phosporus, Potassium, Temperature, Humidity, pH, and Rainfall.
- These conditions are then processed by a preloaded machine learning model to predict a suitable crop for the given conditions.
```bash
N = int(request.form['Nitrogen'])
P = int(request.form['Phosporus'])
K = int(request.form['Potassium'])
temp = float(request.form['Temperature'])
humidity = float(request.form['Humidity'])
ph = float(request.form['pH'])
rainfall = float(request.form['Rainfall'])
feature_list = [N, P, K, temp, humidity, ph, rainfall]
single_pred = np.array(feature_list).reshape(1, -1)
prediction = loaded_model.predict(single_pred)
```
- Once a prediction is determined, the output is matched against a dictionary to retrieve the crop's name and deliver a readable and user-friendly output.
```bash
crop_dict = {1: "Rice", 2: "Maize", 3: "Jute", 4: "Cotton", 5: "Coconut", 6: "Papaya", 7: "Orange",
8: "Apple", 9: "Muskmelon", 10: "Watermelon", 11: "Grapes", 12: "Mango", 13: "Banana",
14: "Pomegranate", 15: "Lentil", 16: "Blackgram", 17: "Mungbean", 18: "Mothbeans",
19: "Pigeonpeas", 20: "Kidneybeans", 21: "Chickpea", 22: "Coffee
```
## Project Demo: https://github.com/vishant007/annadataa/assets/64253459/f674c78e-74be-4144-b244-4e27eb117f7c
## Installation Guide:
- Clone the repo into your device.
- Install required dependencies
- Run the following commands:
```bash
cd Crops-Recommendation-System-ML-AI
flask run
```
- Open new terminal.
- Run the following command
```bash
python manage.py runserver
```
- Happy Hacking!!