Crop and Fertilizer Recommendation System This project aims to provide farmers and agriculture enthusiasts with precise recommendations for crops and fertilizers based on key environmental and soil parameters. By leveraging input data, such as soil composition and regional details, the system helps users make informed decisions to optimize agricultural productivity and sustainability.

Features Crop Recommendation The system suggests the best crop(s) to grow based on:

Nitrogen Percentage: Measures the availability of nitrogen in the soil. Potassium Percentage: Indicates the soil's potassium content. pH Level: Evaluates the soil's acidity or alkalinity. Rainfall (in mm): Determines the suitability of the region based on precipitation levels. State: Helps contextualize the recommendation to local agricultural practices. City: Narrows down specific regional attributes for better accuracy. Fertilizer Recommendation The system identifies the optimal fertilizer for the selected crop based on:

Nitrogen Content: Required for plant growth and chlorophyll production. Potassium Content: Essential for water regulation and enzyme activation. Phosphorus Content: Critical for root development and energy transfer. Selected Crop: Tailors recommendations to the nutritional requirements of the chosen crop. Project Workflow User Input: Users answer a set of targeted questions regarding soil composition, regional details, and their intended crop. Data Processing: The system analyzes the input using pre-trained models or heuristic algorithms. Recommendation Generation: Crop Recommendation: Suggests a list of suitable crops. Fertilizer Recommendation: Provides specific fertilizer combinations tailored to the selected crop. Output: Displays actionable insights in a user-friendly format. Technologies Used Frontend: [React.js/Next.js] for an intuitive and responsive interface. Backend: [Python/Django or FastAPI] to handle data processing and recommendation logic. Database: [MongoDB/PostgreSQL] for storing crop and fertilizer data. Machine Learning: Scikit-learn or TensorFlow for predictive modeling and recommendation algorithms. Deployment: Hosted on [AWS/Vercel/Heroku].