Conformal Prediction Sets for Artificial Intelligence Agriculture Herbicide Resistance Risk Under Sparse Experimental Trial Coverage: A Computer Marginal-Coverage Evaluation

Conformal prediction offers distribution-free marginal coverage for herbicide resistance risk classification, but its guarantees are asymptotic in the number of calibration trials and degrade when experimental coverage is sparse. the present analysis formalises the marginal-coverage shortfall of split-conformal prediction sets for resistance-risk classification under sparse trial coverage and derives a finite-sample correction that restores the nominal guarantee. The analysis treats the calibration set as a sample of experimental trial clusters rather than independent plots, so the effective sample size is governed by the intraclass correlation of resistance outcomes within trials. Under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, a replication-based evaluation of the before-after-control-impact estimator was conducted across 2000 design cells, with cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The evaluation shows that 54 clusters are required to detect a 5 percentage point effect at 80 percent power, and that the nominal 90 percent conformal set attains its stated coverage only when the calibration set contains at least that many clusters. A cluster-robust conformal score is proposed that inflates the calibration quantile by a factor derived from the intraclass correlation, and the resulting sets are shown to recover marginal coverage at the nominal level while remaining informative. The contribution is a coverage-calibrated procedure for resistance-risk prediction that is honest about the sparsity of the trial evidence on which it rests.