Benchmarking Evaluation of Vision-Language Foundation Models for Computer Agriculture Weed Identification Under Fine-Grained Species Tails: An Artificial Intelligence Long-Tail Generalization Bound for Herbicide Misapplication Risk

Vision-language foundation models are increasingly proposed as the perception layer for computer-agriculture weed identification, yet their evaluation protocols assume balanced species coverage that field populations never provide. The framework treats the species distribution as a heavy-tailed frequency law, decomposes zero-shot and few-shot identification error into head, tail and cross-modal alignment components, and derives a high-probability bound on tail-species misidentification that propagates into a bound on expected herbicide misapplication. The bound is stated in terms of the tail exponent, the number of tail species, the vision-language alignment gap and the calibration error of the model's confidence, and it yields a design rule for the minimum tail-species support required to hold misapplication risk below a declared tolerance. A benchmarking protocol is specified that reports tail-stratified accuracy, alignment gap and calibration alongside aggregate accuracy, and the bound is shown to be tight enough to discriminate between evaluation regimes that aggregate accuracy cannot separate. The contribution is a risk-calibrated benchmarking standard for vision-language weed identification that makes the species tail, rather than the aggregate mean, the unit of evaluation.