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Regression models show strong performance in crop yield prediction

By PulseAugur Editorial · [1 sources] ·

A new research paper evaluates five regression models for crop yield prediction, utilizing a dataset of 1 million observations. The study compared multiple linear regression, random forest regression, decision-tree regression, ridge regression, and lasso regression. Results indicated that linear regression models, particularly lasso regression with a 70:30 training-testing split, achieved competitive performance with a high coefficient of determination (R-squared) and low root mean squared error (RMSE), suggesting their effectiveness as baselines for this agricultural task. AI

IMPACT Provides insights into effective regression techniques for agricultural data analysis, potentially improving crop yield forecasting.

RANK_REASON Academic paper published on arXiv detailing a comparative evaluation of regression models for crop yield prediction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Regression models show strong performance in crop yield prediction

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Academic paper published on arXiv detailing a comparative evaluation of regression models for crop yield prediction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Saptak Das, Arijit Sarkar, Soumik Neogi, Suborno Naskar ·

    Crop Yield Prediction Using Regression Models: A Comparative Evaluation of Model Performance

    arXiv:2610.37022v1 Announce Type: cross Abstract: Reliable crop-yield prediction can support agricultural planning and resource allocation. This study evaluates five regression models for predicting crop yield from environmental, crop-related, and agricultural management variable…

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