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In agriculture, efficient and precise fertilizer management is crucial for optimizing crop yield, minimizing environmental impact, and ensuring sustainable farming practices. This paper presents a comprehensive study on fertilizer recommendation systems leveraging a machine learning model named random forest algorithm. The proposed model utilizes various agronomic parameters such as soil type, nutrient content, weather conditions, and crop type to predict the optimal fertilizer requirements. The machine learning algorithm employed demonstrates a high degree of accuracy in predicting fertilizer needs, providing farmers with personalized recommendations tailored to their specific agricultural conditions. By integrating the model into a web app, this approach aims to enhance the overall efficiency of fertilizer application, reduce resource wastage, and contribute to environmentally responsible farming practices. The research findings offer valuable insights into the potential of machine learning in revolutionizing fertilizer management, fostering precision agriculture, and ultimately contributing to global food security and sustainability.

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Fertilizer and Crop Recommendation System using Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • M. Sucharitha,
  • P. H. V. Sesha Talpa Sai,
  • Munmun Gorai,
  • Mithila Mahato,
  • C. Adarsh,
  • Vinodkumar Hiremath,
  • Benu Pradhan,
  • Amiya Bhaumik

摘要

In agriculture, efficient and precise fertilizer management is crucial for optimizing crop yield, minimizing environmental impact, and ensuring sustainable farming practices. This paper presents a comprehensive study on fertilizer recommendation systems leveraging a machine learning model named random forest algorithm. The proposed model utilizes various agronomic parameters such as soil type, nutrient content, weather conditions, and crop type to predict the optimal fertilizer requirements. The machine learning algorithm employed demonstrates a high degree of accuracy in predicting fertilizer needs, providing farmers with personalized recommendations tailored to their specific agricultural conditions. By integrating the model into a web app, this approach aims to enhance the overall efficiency of fertilizer application, reduce resource wastage, and contribute to environmentally responsible farming practices. The research findings offer valuable insights into the potential of machine learning in revolutionizing fertilizer management, fostering precision agriculture, and ultimately contributing to global food security and sustainability.