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Proposes a Task-Conditioned Synthetic Data Generation (TCSDG) algorithm combining a Bayesian Network generator with a transformer-based tabular foundation model to improve ML performance in agricultural prediction tasks, showing consistent improvements over benchmarks.
AI has great potential in agriculture, but its effectiveness depends on clean and complete data foundations; the industry faces unique data challenges from IoT devices, weather feeds, and land-specific variables.