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A major factor in India’s financial expansion is agriculture. The country’s increasing population and continuously shifting climate influences crop productivity and food safety. Crop selection is influenced by a multitude of elements, such as government policy, soil type, rainfall, temperature, market price, and production rate. Numerous adjustments are needed in the agriculture industry in an effort to strengthen the Indian economy. The paper focuses on different types of machine learning algorithms to approximate the crop on different variety of crops in the Indian states. The outcome shows from all the algorithms what we applied; Linear Regression SVM, Decision Tree Ensemble and Neural networks the linear regression and SVM performed better from the remaining algorithms with on 0.853 R2, 0.035 RMSE, and 0.0251 Mean Average Error, The insights are validated using cross-validation procedures, Root Mean Square Error and Mean average error. This effort aims to help farmers address agricultural yield-related problems by using the crop selection approach.

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Supporting of Crop Yield Prediction Using Machine Learning Algorithm Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • S. Vasundhara,
  • Madhavi lata Mangipudi,
  • Supriya Vaddi,
  • Hema Neelam

摘要

A major factor in India’s financial expansion is agriculture. The country’s increasing population and continuously shifting climate influences crop productivity and food safety. Crop selection is influenced by a multitude of elements, such as government policy, soil type, rainfall, temperature, market price, and production rate. Numerous adjustments are needed in the agriculture industry in an effort to strengthen the Indian economy. The paper focuses on different types of machine learning algorithms to approximate the crop on different variety of crops in the Indian states. The outcome shows from all the algorithms what we applied; Linear Regression SVM, Decision Tree Ensemble and Neural networks the linear regression and SVM performed better from the remaining algorithms with on 0.853 R2, 0.035 RMSE, and 0.0251 Mean Average Error, The insights are validated using cross-validation procedures, Root Mean Square Error and Mean average error. This effort aims to help farmers address agricultural yield-related problems by using the crop selection approach.