Sequential Decision-Transformer Inference for Computer Agriculture Variable-Rate Nitrogen Prescription Under Within-Season Sensor Drift: An Artificial Intelligence Regret-Bound Evaluation

Variable-rate nitrogen prescription in precision agriculture increasingly relies on sequential inference from within-season canopy sensors, yet the optical and thermal channels that carry the signal drift as the season advances, so that a policy tuned to the start of the season is not the policy that should be applied at the end. the present analysis treats the prescription problem as a finite-horizon sequential decision problem under a drifting observation kernel and derives a regret bound for a decision-transformer inference policy that conditions on the full history of sensor readings rather than on the current reading alone. The analysis is theoretical and methodological: no field data are used or claimed. A drift-aware regret decomposition separates the irreducible cost of acting under a corrupted observation from the estimable cost of policy mismatch, and the bound is expressed in terms of the drift rate, the horizon, the number of prescription levels and the transformer's context length. A design-based evaluation characterises the operating characteristics of a before-after-control-impact estimator under a declared variance structure with an intraclass correlation of 0.64, a between-cluster standard deviation of 6.0 and a residual standard deviation of 4.5; power was estimated across 2000 design cells over cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points, and 54 clusters are required for 80% power at a 5 percentage point effect. The contribution is a bound that makes the drift-policy trade-off explicit and a design rule that connects it to the cluster counts an evaluation can realistically afford.