What we fixed, measured, and proved — before vs after

In mid-2026 we re-audited our own stack end-to-end: every legacy claim re-tested, every upgrade scored against ground truth, every client deliverable re-verified. Negative results are published below with the same prominence as wins — that is the point.

Signal & detection stack

DimensionBeforeAfter remediationEvidence
Harvest-event detection latencyMonthly composites — median 60 days to confirmationTwo-tier: certified monthly record + 12-day early-warning tier — median 24 days; earlier on 62 of 63 real events63 ground-truthed events, identical detector rule
Species classification (3-species, 185 plots)0.60 balanced accuracy, median/amplitude features0.625 with harmonic phenology features (+2.4 pts; hardest class honestly unchanged)LOO-kNN, byte-identical protocol
Radar frequency diversityC-band only; saturates in mature canopy+ L-band wired (ALOS-2 archive, NISAR ongoing). honest negative no species-ID gain on young stands — documented, not claimed185/185-plot L-band coverage
Biomass anchorNone (radar proxy only)GEDI LiDAR wired + correctly scoped: anchors MATURE stands; young rotations sit at LiDAR noise floor scoping finding36 dated stand-valid footprint pairs
Revisit cadence~15-day effective (single platform)~11-day and improving as new Sentinel-1 platforms come online; dual-orbit module region-awarescene census 2024 vs 2026
Ocean color (aquaculture)Single aging sensor, 8+ months data lagMulti-sensor merged product, 5+ months fresher — with coastal cross-product disagreement disclosed, resolved by pilot log-calibration27 vs 20 monthly values at the farm

Validation infrastructure (did not exist before)

CheckResult
Determinism: do delivered client findings reproduce exactly from archived data?PASS pulpwood 185/185 plots · sugarcane stress flags 4/4
Live persistence: do findings hold on fresh satellite data weeks later?PASS tea weak-block re-confirmed (+1 new decliner surfaced) · 17/20 rubber scout pins persist, 3 marginal flagged "verify-first"
Systemic findingAbsolute-threshold detectors are knife-edged at their margins (seen independently in two products) → two-tier detection + persistence checks adopted stack-wide

Quantum R&D track — the same discipline, harder

DimensionBeforeAfter remediation
Evidence claims"Quantum kernel ahead in all 3 hardware runs" (single runs, no error bars)Rigorous re-analysis: ONE statistically significant alignment result (crop-ID, p=0.0005); two legacy edges shown to be noise; no accuracy advantage anywhere once classical baselines were properly tuned. All packs re-labeled accordingly.
Benchmark scale22-24 samples, 4-qubit generic circuits185 real plots, 3 classes, 10-qubit design; competitive-but-not-superior documented across 10 splits; negative results (alignment-training overfits; projected kernels underperform) published
Hardware honestySome simulator work ambiguously presented (since quarantined)Hardware-vs-simulation agreement measured on a real QPU: r = 0.998 (preliminary, 12 circuits); budget gate stopped an over-budget run automatically; full certification awaits free research compute
The claim we makeImplied advantage"Real hardware, honest baselines, one significant alignment result, no advantage claimed." The R&D track signals depth — the products stand entirely on classical physics.
Why publish our negatives? Because a monitoring vendor you cannot audit is just another opinion. Every row above traces to a scripted, re-runnable check against ground truth we did not choose after the fact. Ask us for the scorecard — we bring it to diligence meetings.
AgriCPS · validation scorecard maintained continuously · figures as of August 2026