Backward-Error-Bounded Quantization of Artificial Intelligence Agriculture Disease Classifiers Under Field Sensor Bit-Depth Reduction: A Computer Worst-Case Confidence-Distortion Guarantee

Deploying convolutional disease classifiers on field-deployed agricultural sensors forces a reduction in the bit-depth of the input representation, and the resulting perturbation of the classifier's logit vector is usually treated as an empirical nuisance rather than as a quantity that can be bounded. The framework is formalised as a set of definitions, a propagation lemma, a worst-case confidence theorem and a bit-depth selection corollary, and it is evaluated through a replication-based design study of a before-after-control-impact estimator under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64). Power was estimated across 2000 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 54 clusters were required for 5 percentage points at 80% power. The framework proposes a certificate that a field-deployed classifier can carry alongside its weights, and the design study characterises the operating characteristics that such a certificate must respect when it is validated in the field.