CloudBorn Aero — Flight Intelligence Software

See What Your Crops Can't Tell You Multispectral Analysis & Machine Learning Crop Detection

Our autonomous flight software captures five spectral bands per frame, processes them through trained ML models in real time, and delivers actionable crop health intelligence before your drone lands.

475nm 560nm 660nm 735nm 850nm ANOMALY
01

Capture

5-band synchronized imaging

02

Calibrate

DLS irradiance normalization

03

Compute

Vegetation index generation

04

Detect

ML anomaly classification

05

Deliver

GPS-tagged field reports

Five Bands, One Pass

Each camera module captures five distinct wavelengths simultaneously — synchronized to GPS PPS for centimeter-accurate geotagging of every pixel.

475
NANOMETERS
Blue
Atmospheric correction and chlorophyll absorption depth
560
NANOMETERS
Green
GNDVI calculation and visual reference compositing
660
NANOMETERS
Red
Primary NDVI component — chlorophyll absorption peak
735
NANOMETERS
Red Edge
Highest correlation with chlorophyll concentration and early stress
850
NANOMETERS
Near-Infrared
Cell structure reflectance — canopy density and biomass estimation

From Reflectance to Insight

Raw spectral data is transformed into standardized vegetation indices — each tuned to reveal different aspects of crop health.

(NIR − Red) / (NIR + Red)

NDVI — Normalized Difference Vegetation Index

The industry standard for overall plant health and vigor. Values range from -1 to +1, with healthy vegetation typically between 0.6 and 0.9. Detects stress weeks before it's visible to the eye.

Overall Health Biomass Vigor
(NIR − Green) / (NIR + Green)

GNDVI — Green Normalized Difference

More sensitive to chlorophyll concentration than standard NDVI. Excels at detecting nitrogen deficiency and estimating leaf area index in dense canopies where NDVI saturates.

Nitrogen Status Chlorophyll LAI
(NIR − RedEdge) / (NIR + RedEdge)

NDRE — Normalized Difference Red Edge

The red edge band is the most sensitive indicator of early chlorophyll changes. NDRE catches nutrient deficiency, disease onset, and water stress earlier than any other index.

Early Stress Disease Onset Precision
(NIR / Red) − 1

RVI — Ratio Vegetation Index

Simple but effective ratio for distinguishing vegetation from bare soil. Useful for stand count estimation, emergence uniformity assessment, and identifying gaps in crop coverage.

Stand Count Emergence Coverage
1.5 × (NIR − Red) / (NIR + Red + 0.5)

SAVI — Soil Adjusted Vegetation Index

Corrects for soil background reflectance in sparse canopies. Essential for early-season scans when crop cover is thin and soil is still exposed between rows.

Early Season Sparse Canopy Soil Correction
2.5 × (NIR − Red) / (NIR + 6R − 7.5B + 1)

EVI — Enhanced Vegetation Index

Uses the blue band for atmospheric correction and resists saturation in high-biomass fields. Preferred over NDVI for dense mid-season canopies where standard indices plateau.

High Biomass Atmospheric Correction Mid-Season

Trained on Real Fields, Not Stock Photos

Our detection models are trained on multispectral data collected from actual Missouri agricultural operations — real crops, real conditions, real problems. The system learns what healthy looks like for your region and flags everything that doesn't match.

  • Anomaly Detection

    Flags reflectance patterns that deviate from the healthy baseline — catches problems before visible symptoms appear.

  • Zone Classification

    Automatically segments fields into management zones based on spectral signatures — healthy, stressed, critical, bare soil.

  • Temporal Tracking

    Compares scans over time to detect rate-of-change in crop health — distinguishes chronic decline from acute events.

  • Real-Time Inference

    GPU-accelerated processing at 30 FPS across four concurrent camera streams. Results available before the drone returns home.

Processing Architecture
Input Layer

4× multispectral streams → NVDEC hardware decode → GPU memory

Pre-Processing

DLS irradiance normalization → band alignment → reflectance calibration

Index Computation

NDVI, GNDVI, NDRE, SAVI, EVI — computed per-pixel on GPU

ML Classification

Trained detection model → anomaly scoring → zone segmentation

Output

GPS-tagged health maps → prescription maps → field reports

Real-Time, In the Field

Trailer-mounted GPU servers process all four camera streams live during flight — no waiting for post-flight batch jobs.

30
Frames Per Second
Per-stream capture and inference rate across all spectral bands
Concurrent Streams
Simultaneous multispectral feeds from overlapping camera modules
120
Inferences / Second
Total frame throughput across the full detection pipeline
24GB
GPU VRAM
RTX A5000 handles decode, index computation, and model inference

What the Models Find

Each detection type is trained on region-specific data and continuously improved with every field scan.

Nutrient Deficiency

Nitrogen, phosphorus, and potassium stress patterns identified through chlorophyll and red edge analysis.

Water Stress

Early wilting and drought response detected through canopy temperature proxies and NIR reflectance shifts.

Disease Onset

Fungal infections, bacterial spots, and viral symptoms flagged before visible lesions appear on leaf tissue.

Pest Damage

Insect feeding patterns and defoliation identified through canopy density changes and spectral anomalies.

Stand Gaps

Missing plants, failed emergence, and poor stand uniformity mapped for replanting decisions.

Weed Pressure

Spectral signature differences between crop and weed species enable targeted herbicide application maps.

Lodging Detection

Flattened or leaning crop areas identified through canopy height and reflectance angle changes.

Yield Estimation

Biomass indices correlated with historical yield data to produce pre-harvest field-level yield predictions.

Compute That Goes Where You Go

A trailer-mounted GPU server processes every frame in real time — no cloud uploads, no internet dependency, no waiting. Your data stays on your hardware.

Portable, Private, Powerful

All inference runs on a field-deployable 2U server with a dedicated GPU. Gigabit Ethernet transfers data directly from the drone's FPGA — no wireless bottleneck, no cloud latency, no third-party data access.

Your multispectral data never leaves your equipment. Reports generate locally and stay under your control.

RTX A5000
GPU Compute
128GB
System RAM
10GbE
Data Link
NVMe
Fast Storage
2U
Server Form Factor
3kW
Generator Ready

Ready to See Your Fields Differently?

From spectral capture to ML-powered health maps — CloudBorn Aero gives you the intelligence to act before problems become losses.

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