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This repository contains a crop recommendation model built using machine learning to help farmers choose optimal crops based on environmental factors like soil type, climate, and region.

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FarmSathi: Smart Agricultural Recommendation System

FarmSathi is an AI-powered agricultural assistant designed to support farmers in making informed decisions. The system provides comprehensive recommendations for crops, fertilizers, pesticides, and crop disease management, ensuring better productivity and sustainable farming practices.
FarmSathi integrates advanced machine learning models to deliver the following features:

  • Crop Recommendation: Suggests the best crops based on soil and environmental conditions.
  • Fertilizer Recommendation: Recommends fertilizers tailored to soil nutrient levels and crop type.
  • Pesticide Guidance: Provides information on pest control measures and pesticide usage.
  • Disease Detection: Identifies crop diseases from images and suggests remedies.
  • Data Insights: Visualizes soil and crop data trends for better decision-making.

Development Progress 🚧

FarmSathi is currently under development, and the following features have been implemented:

  • Crop and fertilizer recommendation models.
  • Streamlit-based web application for easy user interaction.
  • Exploratory Data Analysis (EDA) to understand key patterns.

Upcoming Features:

  • Integration of pesticide recommendation and disease detection.
  • Expansion of the knowledge base with pest and disease libraries.
  • Scalability for additional features like irrigation suggestions.

How to Run 🛠️

1. Clone the repository:

git clone https://github.com/Mohanty-Hitesh-4495/Crop-Recommendation-System.git

2. Install the required dependencies from requirements.txt.

pip install -r requirements.txt

3. Run the Streamlit application:

streamlit run app.py

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This repository contains a crop recommendation model built using machine learning to help farmers choose optimal crops based on environmental factors like soil type, climate, and region.

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