Transforming Agriculture with Machine Learning
A machine learning-based platform designed to revolutionize farming practices. With a focus on three core features: crop prediction, yield forecasting, and disease detection, this platform harnesses the power of advanced machine learning algorithms to support farmers in making informed decisions that improve productivity, sustainability, and overall farm management.
Using data from multiple sources, including soil quality, weather patterns, and environmental conditions, the platform predicts the most suitable crops for specific regions. By analyzing historical trends, we provide actionable insights to farmers, enabling them to choose the right crops for optimal growth, improving both yield and quality.
Our yield forecasting models take into account soil composition, weather patterns, crop type, and historical performance data to predict the potential yield of crops. This feature helps farmers plan better and reduce wastage, ensuring they can maximize production and profitability.
Leveraging machine learning, the platform identifies early signs of diseases in crops by analyzing various factors, such as plant health, weather conditions, and previous disease outbreaks. Early detection allows farmers to take proactive measures to prevent widespread crop damage, reducing the use of pesticides and increasing crop longevity.
- HTML, CSS, JavaScript: The core technologies for building a responsive and interactive frontend.
- Express: A fast and minimal web framework used for routing and server-side logic in this full-stack application.
- Python: The primary language for developing the machine learning models that power the crop prediction, yield forecasting, and disease detection features.
- Flask or FastAPI: Lightweight web frameworks used to integrate and serve the machine learning models through API endpoints for smooth interaction with the frontend.
- MongoDB: A NoSQL database used to store essential data, including user profiles, prediction results, and other project-related information. It’s chosen for its flexibility and scalability.
- Scikit-learn: A library for machine learning that provides simple and efficient tools for data analysis and predictive modeling.
- Pandas: A powerful data manipulation tool that simplifies working with structured data.
- NumPy: A fundamental package for scientific computing that enables fast array operations and numerical analysis.
To set up and run this project locally, follow these steps:
- Clone the repository:
git clone https://github.com/yourusername/Harvest-Horizon.git- Navigate into the project directory:
cd Harvest-Horizon- Install backend dependencies:
Move to thebackend-pyfolder and install the required Python libraries:
pip install -r requirements.txt- Install frontend dependencies:
Go to the root directory and install the frontend dependencies:
npm install- Start the backend server:
In thebackend-pyfolder, run the following command to start the backend:
python app.py- Start the frontend server:
In the root directory, run this command to launch the frontend server:
npm run bothVisit the app at: http://localhost:8000
The backend is hosted on port 5000 and MongoDB on port 2000.
We welcome contributions from everyone! Whether it's a bug fix, a new feature, or just a suggestion, feel free to fork the repository, open an issue, or submit a pull request. All contributions are appreciated.
Please make sure to follow the standard coding practices and write clear commit messages for better collaboration.
This project is licensed under the MIT License. You can find more information in the LICENSE file. This open-source license allows users to freely use, modify, and distribute the software.
A glimpse into the crop prediction feature, helping farmers choose the best crops.
harvest.horizion.mp4
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This project is a collaboration between Harman Deep Singh and Manav Gupta. We would like to extend our special thanks to Manav for his incredible contributions to the development and growth of this project. Together, we aim to leverage machine learning to make agriculture smarter and more efficient.
Let's make agriculture smarter, together! 🌾🌱