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Machine Learning Based Agricultural Price Forecasting for Major Food Crops in India Using Environmental and Economic Factors

Authors: P. Ankit Krishna,Gurugubelli V. S. Narayana,Siva Krishna Kotha,Debabrata Pattnayak
Journal: The 3rd International Online Conference on Agriculture
Publisher: MDPI
Publish date: 2026-1-12
ISSN: Not Available DOI: 10.3390/blsf2025054007
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The abstract reports XGBoost performance as R²=0.94, RMSE=12.8, MAE=8.6, yet Table 1 states R²=0.988, RMSE=9.26, MAE=7.22 for the same model. Which set of metrics is correct? Such a significant discrepancy cannot be attributed to rounding and suggests a critical lack of reproducibility or a reporting error that fundamentally misrepresents your model’s capability.

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