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Fertilizer recommendation plays a crucial role in optimizing crop yield while minimizing resource wastage. The integration of machine learning techniques enables precise fertilizer prediction based on soil and environmental conditions, leading to improved agricultural productivity. However, traditional methods often result in overuse or underuse of fertilizers, negatively impacting soil health and crop growth.This study employs various machine learning algorithms, including RandomForest, XGBoost, LightGBM, HistGradientBoosting, CatBoost, and Neural Networks, to classify and recommend fertilizers based on soil parameters. The models were trained on a synthetic fertilizer dataset containing diverse soil compositions and fertilizer requirements.Experimental results indicate that Neural Networks outperform tree-based models, achieving the highest testing accuracy of 84.10%, demonstrating strong generalization capabilities. Accuracy and loss trends over epochs confirm stable learning, while a confusion matrix reveals minimal misclassifications. This study highlights the effectiveness of deep learning in optimizing fertilizer recommendations, contributing to more sustainable and efficient agricultural practices.

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Optimized Machine Learning Models for Fertilizer Recommendation Using Feature Engineering and Neural Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Nisarg Chaudhari,
  • Urva Dave,
  • Zeel Patel,
  • Manasvi Vachhani,
  • Dweepna Garg,
  • Bhavika Patel,
  • Kashyap Patel,
  • Parth Goel

摘要

Fertilizer recommendation plays a crucial role in optimizing crop yield while minimizing resource wastage. The integration of machine learning techniques enables precise fertilizer prediction based on soil and environmental conditions, leading to improved agricultural productivity. However, traditional methods often result in overuse or underuse of fertilizers, negatively impacting soil health and crop growth.This study employs various machine learning algorithms, including RandomForest, XGBoost, LightGBM, HistGradientBoosting, CatBoost, and Neural Networks, to classify and recommend fertilizers based on soil parameters. The models were trained on a synthetic fertilizer dataset containing diverse soil compositions and fertilizer requirements.Experimental results indicate that Neural Networks outperform tree-based models, achieving the highest testing accuracy of 84.10%, demonstrating strong generalization capabilities. Accuracy and loss trends over epochs confirm stable learning, while a confusion matrix reveals minimal misclassifications. This study highlights the effectiveness of deep learning in optimizing fertilizer recommendations, contributing to more sustainable and efficient agricultural practices.