Crop360 | Holistic Plant & Crop Management | Final Year Major Project

(Diagnose | Decide | Predict) Live Output: Oct 2024 – Dec 2024

Live Demo:

https://www.linkedin.com/posts/umar-alam-khan_python-tensorflow-scikit-activity-7257497469440139264-RRmV?utm_source=share&utm_medium=member_desktop&rcm=ACoAAD-syYsBKd9HWIEqgk_fRUjb1v5b9uRQhiU

Description

Crop360 is an AI-driven agricultural platform that provides comprehensive support to farmers and agronomists. It helps them diagnose plant diseases, recommend suitable crops, and predict crop yields by analyzing data such as soil pH, sunlight exposure, geolocation, temperature, and humidity. Trained on 200,000+ datasets, Crop360 delivers actionable insights with a 97% disease detection accuracy, aiming to reduce crop losses by up to 30%.

📸 Project Screenshots

Key Features

Plant Disease Detection: Uses Convolutional Neural Networks (CNNs) to detect and classify plant diseases from leaf images with 97% accuracy.

Crop Recommendation: Suggests the best crop to cultivate based on:

Soil characteristics: pH, type, nutrients

Climate data: rainfall, temperature, humidity

Geocoordinates and sunlight exposure

Yield Prediction: Estimates crop productivity using regression models trained on historical agricultural data.

User Interface: Built with Flask and Streamlit for a smooth and accessible user experience.

Technologies Used

1. Python: Core programming language
2. Jupyter Notebook: Model development and testing
3. TensorFlow, Keras: Deep learning model implementation
4. scikit-learn: Machine learning and data modeling
5. NumPy, Pandas: Data preprocessing and manipulation
6. Matplotlib, Seaborn: Data visualization
7. Streamlit, Flask: Web-based deployment
8. CNNs: For image-based disease detection
9. SDLC: Follows structured software lifecycle design

Folder Structure Explanation

csharp Copy Edit Crop360/ │ ├── app.py ├── requirements.txt │ ├── static/ │ └── img.jpg # Store images, stylesheets, etc. │ ├── templates/ │ └── index.html # Store HTML templates for rendering │ └── model/ └── crop_model.pkl # Trained ML model

Image & Template Integration in Flask In your HTML (inside templates/index.html):

html Copy Edit

In your Flask route (inside app.py):

python Copy Edit from flask import Flask, render_template app = Flask(name)

@app.route('/') def home(): return render_template('index.html')

Installation and Usage bash Copy Edit

Step 1: Clone the repository

git clone https://github.com/UMAR-ALAM-786/Crop360.git

Step 2: Navigate to the project directory

cd Crop360

Step 3: Install dependencies

pip install -r requirements.txt

Step 4: Run the application

python app.py

Step 5: Access it via browser

http://localhost:5000

Future Enhancements Integration of real-time weather APIs Inclusion of crop market prices to guide economically viable decisions Development of mobile application (Android/iOS) User feedback loops to continuously retrain and improve model accuracy Multilingual support for broader accessibility

Contributions Contributions are highly welcome! Feel free to submit suggestions, issues, or pull requests via GitHub Issues.

Acknowledgements We acknowledge the support of the agricultural research community, data contributors, and farmers who helped shape the foundation of Crop360.

Contact Email: alamumar91@gmail.com

GitHub: UMAR-ALAM-786