Benchmarking Evaluation of Vision-Language Foundation Model Adaptation for Computer Agriculture Weed Identification Under Few-Shot Extension Conditions: An Artificial Intelligence Generalization Bound for Herbicide Misapplication Safety

Vision-language foundation models adapted to weed identification in computer agriculture are typically evaluated by top-1 accuracy on held-out images, a metric that does not bound the agronomic consequence of a misclassification. The framework formalises the adaptation problem as risk minimisation over a hypothesis class of vision-language prompts, derives a PAC-Bayes bound that links few-shot sample size, prompt-space complexity and the divergence between source and target weed floras to an upper bound on the probability of herbicide misapplication, and specifies a benchmarking protocol that reports this bound alongside conventional accuracy. The bound is decomposed into a transfer term, a few-shot estimation term and a distribution-shift term, each of which corresponds to a distinct benchmarking condition that current evaluation practice omits. A worked instantiation for a four-class weed/non-weed decision problem shows how the bound responds to shot count, prompt complexity and shift magnitude, and identifies the shot count below which the bound exceeds a declared safety threshold. The contribution is a shift from accuracy benchmarking to risk-bounded benchmarking for safety-critical agricultural vision, with implications for how few-shot extension deployments should be evaluated before herbicide recommendations are issued.