Certified Robustness Bounds for Artificial Intelligence Agriculture Disease Classifiers Under Field Image Corruption: A Computer Smoothing-Based Verification Evaluation

Field-deployed crop disease classifiers are routinely evaluated on clean benchmark images, yet the corruption processes that dominate smallholder photography—motion blur, sensor noise, compression artefacts, variable illumination and occlusion—are rarely bounded in any formal sense. The framework treats the classifier as a deterministic map from a smoothed input distribution to a label, applies randomised smoothing to obtain a closed-form robustness certificate, and propagates the certificate through a corruption model parameterised by blur radius, noise variance and compression quality. The analysis is methodological and theoretical: no field images were collected and no classifier was retrained. Instead, the paper specifies the certificate construction, derives the conditions under which the bound remains non-vacuous for realistic corruption magnitudes, and characterises the trade-off between certified radius and clean accuracy. The principal contribution is a formal bridge between the corruption statistics reported in agricultural imaging studies and the certified radius that a smoothing-based verifier can guarantee, together with an explicit statement of the assumptions under which the bridge holds. The framework is intended as a pre-deployment verification protocol for disease classifiers intended for field use, and it identifies the corruption regimes in which certification is currently infeasible.