Table Of Content
Abstract
The project "Crop Recommendation and Yield Prediction for Indian States" aims to leverage machine learning and web technologies to provide efficient agricultural recommendations and yield predictions. Utilizing a combination of React for the frontend, Firebase for database management, and Python for backend processes, this project integrates a Random Forest Algorithm to enhance predictive accuracy. The system comprises several user-focused modules, including features for user login/signup, crop recommendation based on predictive analytics, yield prediction quantified in numbers, and a rain probability forecast for the next three days to optimize fertilizer use, leveraging a weather API. This project aspires to empower farmers with actionable insights, thereby promoting sustainable agricultural practices and enhancing crop productivity in India.
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Technologies Used
| Frontend | React JS |
| Backend | Firebase, Python |
| Algorithm | Random Forest Classification |
| Accuracy | 98% |
| Project ID | ML007A |
Modules
- User
- 🌾 Login/Signup
- 🌾 Crop Recommendation (Prediction as crop name)
- 🌾 Yield Prediction (Prediction as crop yield in numbers)
- 🌾 Rain probability for next 3 days for efficient fertilizer use (Use weather API)