Benchmarking Evaluation of Event-Camera Optical-Flow Estimation for Computer Agriculture Row-Crop Navigation Under Dust-Occlusion Transients: An Artificial Intelligence Lateral-Error Bound for Implement Guidance Safety

Event cameras report per-pixel brightness changes asynchronously and have been proposed as a sensing modality for row-crop implement guidance where dust occludes frame-based cameras. the present analysis treats the guidance problem as a benchmarking question rather than a hardware question: given a declared event-camera optical-flow estimator and a declared dust-occlusion transient model, what lateral-error bound can be certified for a rear-mounted implement, and how many independent field clusters must a before-after-control-impact benchmark contain before that bound is estimable at conventional power? No observational data are used or claimed. The analysis characterises the operating characteristics of a before-after-control-impact design under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The design attains 80 per cent power at 54 clusters for a 5 percentage-point effect. A lateral-error bound is derived that separates estimator variance, occlusion-induced bias and cluster-level confounding, and the bound is shown to be identifiable from the benchmark only when the cluster count exceeds the value at which the minimum detectable effect falls below the safety-relevant threshold. The contribution is a certification logic that ties benchmark design to a stated implement-guidance safety bound, together with the power and minimum-detectable-effect surfaces that make the logic operational.