Benchmarking Evaluation of Tensor-Network Compression for Computer Agriculture Disease Classifiers Under On-Device Memory Ceilings: An Artificial Intelligence Accuracy-Retention Audit for Field Diagnostic Safety

Tensor-network compression promises to fit convolutional disease classifiers into the memory ceilings of field-deployable diagnostic devices, but the accuracy cost of compression is usually reported as a single post-hoc number rather than as a bounded retention guarantee. the present analysis treats compression as a design problem with an auditable operating characteristic: given a declared memory ceiling, what fraction of the uncompressed diagnostic accuracy survives, and with what uncertainty? We formalise tensor-train decomposition of convolutional kernels as a rank-constrained projection, derive a retention bound that links bond dimension, kernel spectrum and the resulting classification margin, and then characterise the operating characteristics of a benchmarking protocol that estimates retention under a declared variance structure. The protocol is a before-after-control-impact design with between-cluster SD 6.0, residual SD 4.5 and intraclass correlation 0.64, evaluated across 2000 replications per design cell at cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. Power reaches conventional levels only at 56 clusters for a 5-percentage-point retention effect, which exceeds every cluster count in the declared grid; the minimum detectable effect at 80% power therefore remains above the 5-percentage-point threshold throughout the grid. The contribution is a retention bound plus a power-calibrated audit protocol that tells practitioners when a compression benchmark is informative and when it is underpowered, and that reframes field diagnostic safety as a property of the evaluation design rather than of the compressor alone.