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AgriSpectra-Q
FROZEN SCIENTIFIC BENCHMARK90 Runs · 6 Models · 3 Scenes

Benchmark Results

Six models evaluated across 3 real EnMAP scenes with 5 random seeds each. These are frozen scientific results — not live analysis outputs. For a live run, go to Intelligence → Run Analysis.

SCIENTIFIC INTERPRETATION

Competitive numerical result

AgriSpectra-Q has the highest mean F1 (96.40%) among all six evaluated systems.

Statistical superiority over HSI-RF not established

Bootstrap 95% CI [−0.0012, +0.0027] crosses zero. Not statistically significant.

Benchmark target is a Spectral Anomaly Proxy — not independently labelled disease or pest outcome.

96.40%
Mean F1
3 scenes × 5 seeds
99.47%
PR-AUC
Precision-Recall
99.87%
ROC-AUC
Discrimination
0.73%
Calibrated ECE
Prob. calibration

Six-Model Benchmark

ModelMean F1PR-AUCROC-AUC
AgriSpectra-QOur System
96.40%
99.47%
99.87%
Adaptive Classical
96.34%
99.47%
99.86%
HSI-RF
96.31%
99.49%
99.87%
48-band XGBoost
95.22%
99.23%
99.80%
Spectral XGBoost
94.78%
99.24%
99.81%
Current Hybrid
89.98%
96.16%
98.75%

AgriSpectra-Q — Per-Scene Results

SceneF1PrecisionRecallROC-AUC
Scene 01 (DT0000205230)
98.47%
98.23%
98.73%
99.98%
Scene 02
95.42%
95.80%
95.10%
99.83%
Scene 03
95.30%
94.81%
95.83%
99.79%

What the Benchmark Tells Us

AgriSpectra-Q achieves 96.40% mean F1 — highest numerical result among all six evaluated systems.

Scene 01 (DT0000205230) delivers 98.47% F1, reflecting high spectral contrast in the Al Ain oasis region.

Calibration ECE of 0.73% after post-hoc scaling — well-calibrated probabilities for decision support.

At 10% inspection budget, the system achieves ~49% positive recall — ~5× better than random sampling.

Ablation studies confirm the quantum-inspired component provides measurable contribution.

Paired bootstrap CI [−0.0012, +0.0027] vs HSI-RF at n=10,000 replicates.

5 Random Seeds
11, 22, 33, 44, 55

Statistical stability across initialisation variance

3 Real EnMAP Scenes
UAE & Gulf region

No simulated data — all results on actual EO imagery

Paired Bootstrap
n = 10,000 replicates

Rigorous significance testing for model comparisons

Run a live spectral-priority analysis

Apply the live engine to real EnMAP scenes and generate georeferenced priority zones.

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