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In India, agriculture plays a crucial role in boosting the economy. However, traditional methods of cultivation often result in poor crop yields due to bad weather conditions, leading to financial losses for farmers. Machine learning techniques have shown promise in improving crop prediction accuracy, surpassing the traditional methods. We have collected a crop dataset with parameters such as temperature, humidity, rainfall, and pH, and trained various machine learning algorithms including K-NN, RF, DTC, SVM, XGBoost. Our experimental results indicate that the XGBoost ensemble classifier outperforms with the highest accuracy of 94.67% among all classifiers, making it a reliable option for crop prediction. All performance metrics obtained good as comparison to other classifiers.

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Optimizing Crop Recommendations in Indian Agriculture Using Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Lipsa Mishra,
  • A. V. S. Pavan Kumar,
  • Neelamadhab Padhy

摘要

In India, agriculture plays a crucial role in boosting the economy. However, traditional methods of cultivation often result in poor crop yields due to bad weather conditions, leading to financial losses for farmers. Machine learning techniques have shown promise in improving crop prediction accuracy, surpassing the traditional methods. We have collected a crop dataset with parameters such as temperature, humidity, rainfall, and pH, and trained various machine learning algorithms including K-NN, RF, DTC, SVM, XGBoost. Our experimental results indicate that the XGBoost ensemble classifier outperforms with the highest accuracy of 94.67% among all classifiers, making it a reliable option for crop prediction. All performance metrics obtained good as comparison to other classifiers.