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This study aims to estimate wheat production using satellite and meteorological data through machine learning.

June 22, 2026Open Access

ML-Based Wheat Production Estimation using Two Decades of Remote Sensing & Meteorological Time-Series Data across Five Major Indian States

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This study aims to estimate wheat production using satellite and meteorological data through machine learning.
Integrated satellite-derived data from MODIS and GLDAS over two decades (2003-2025).
Applied decision-rule based thresholding to NDVI for classifying wheat and non-wheat areas.
Utilized tree-based machine learning algorithms (DTR, RFR, ETR, XGBR) for production prediction.
XGBR model outperformed others with an average error of 5.6% compared to 10.3% for DTR, 8.3% for RFR, and 7.4% for ETR.
Predicted production statistics were validated against UPAg data, confirming robustness of XGBR.
The integration of remote sensing with machine learning shows promise for agricultural forecasting challenges.

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Randomized trial predicts wheat production in Indian states, suggesting efficient harvest management techniques.

Pareek et al. (2026) studied this question.

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