https://github.com/kalash9630/crop-advisor

I've developed a crop prediction model using the Random Forest Classifier machine learning algorithm, integrating Soil Metrics and Weather Data to recommend optimal crops. The primary objective is to improve farmers' yields, enhance financial stability, and promote soil health, ultimately resulting in increased profitability.
https://github.com/kalash9630/crop-advisor

machine-learning-algorithms pandas-library skit-learn

Last synced: 12 days ago
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I've developed a crop prediction model using the Random Forest Classifier machine learning algorithm, integrating Soil Metrics and Weather Data to recommend optimal crops. The primary objective is to improve farmers' yields, enhance financial stability, and promote soil health, ultimately resulting in increased profitability.

• Host: GitHub
• URL: https://github.com/kalash9630/crop-advisor
• Owner: Kalash9630
• Created: 2023-10-06T11:02:24.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-11-08T11:02:18.000Z (about 2 months ago)
• Last Synced: 2024-11-08T11:29:32.808Z (about 2 months ago)
• Topics: machine-learning-algorithms, pandas-library, skit-learn
• Language: Jupyter Notebook
• Homepage: https://crop-advisor.streamlit.app/
• Size: 13.2 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Crop-Advisor

**LINK** :  https://crop-advisor.streamlit.app/

* I've developed a crop prediction model using the **Random Forest Classifier Machine Learning Algorithm**.

* This model leverages **Soil Metrics** (nitrogen, phosphorous, potassium, pH) and **Weather Data** (temperature, humidity, rainfall) to accurately recommend the optimal crop for a specific field.

* The application of this model offers farmers the potential to **increase their yields**, **improve financial stability**, and **maintain soil health and sustainability**, ultimately leading to enhanced profitability.

* The dataset incorporates diverse soil measurements from different fields, with the 'label' column **indicating the best crop** based on these measurements.

* The primary objective of the project is to create a robust multi-class classification model that seamlessly integrates weather data, thereby **improving the precision and accuracy** of crop predictions.

 

* Key benefits of this model include :

  

     **1) Yield Enhancement:**\

           The Random Forest Classifier optimizes crop recommendations based on the intricate interplay between soil characteristics and weather conditions, contributing to improved yields.

   

     **2) Financial Stability:**\

            By providing accurate predictions, the model assists farmers in making informed decisions that mitigate risks associated with environmental factors and market fluctuations, leading to greater financial stability.

    

     **3) Soil Health and Sustainability:**\

             The model's recommendations, rooted in soil metrics, help maintain nutrient balance, prevent overuse of specific elements, and promote sustainable agricultural practices, ensuring the long-term health of the soil.

    

     **4) Profitability Increase:**\

             The precision in crop selection and resource optimization facilitated by the Random Forest Classifier directly translates to higher profitability for farmers.

This initiative aims to empower farmers by offering tailored recommendations for optimal crop selection based on real-world conditions. The integration of weather data enhances the model's predictive accuracy, making it a valuable tool for sustainable and profitable agriculture.