Agricheck is an innovative agricultural project that utilizes technology and data-driven solutions to enhance farming practices and improve crop production. It aims to bridge the gap between traditional farming practices and modern agricultural science by providing farmers with valuable insights through its advanced features.
The project Agricheck was inspired by the pressing need to improve agricultural practices and address the challenges faced by farmers worldwide. It aimed to harness the power of technology and data-driven solutions to enhance crop production, optimize resource utilization, and ultimately contribute to sustainable and efficient farming.
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Crop Prediction: Leveraging historical weather data, soil characteristics, and crop performance metrics, Agricheck provides accurate predictions of future crop yields. This helps farmers make informed decisions about planting schedules, resource allocation, and marketing strategies.
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Fertilizer Recommendation: By integrating soil analysis data, crop requirements, and best practices in nutrient management, Agricheck offers personalized fertilizer recommendations tailored to specific crops and soil conditions. This promotes sustainable and efficient farming practices.
- Backend: Django
- Frontend: HTML, CSS, JavaScript
- Machine Learning: Integrated ML model for crop prediction
- API integration
- Bootstrap for styling
One of the challenges I faced during the project was integrating the ML model with the backend. This was overcome through research, articles, and YouTube tutorials, resulting in a successful integration.
I'm proud of developing the project within the given timeframe and leveling up various skills including web development and machine learning. Agricheck was recognized as one of the Top 20 projects in the TurtleHack Hackathon 2023 on Devpost. View Project Gallery
Check out my project's demonstration on Devpost: Agricheck on Devpost
The project provided insights into agricultural problems in India and showcased how technology can effectively solve these issues. It highlighted the potential of data analysis and technology in addressing complex challenges.
- Improve ML model accuracy for crop prediction.
- Deploy the project on the internet to make it accessible for farmers.
- Scale the product by training the ML model for different countries.
Your contributions are welcome! If you're interested in collaborating or improving the project, feel free to reach out.
- API
- Bootstrap
- CSS3
- Data Science
- Django
- HTML5
- JavaScript
- Machine Learning