Cognition Substrate · v1.0

AI · Quantum · Climate Neural OS

A single cognitive fabric where soil microbiome, water, livestock, climate signals, and farmer livelihood orbit one quantum-attention core. Click any node to inspect.

Node Sample Telemetry

Eight specialised network domains

Filter by domain to see matched telemetry and the live recommendation engine output.

live

🌱 Soil

Microbiome · OC%

16S/ITS · NPK · pH

Reco · Apply 220 kg/ha gypsum + Trichoderma slurry to lift OC by +0.18%.

live

💧 Water

ET₀ · Quality

FAO-56 Penman-Monteith

Reco · Hold irrigation 36h — ET₀ projected to drop 22% post-monsoon pulse.

live

🌦️ Climate

GDD · Monsoon

IMD + Sentinel + AWS

Reco · Shift sowing window by +6 days; GDD accumulation 11% below baseline.

live

🐄 Livestock

Heat · Disease

THI · Thermal CNN

Reco · THI 79 — deploy foggers 12:00-16:00 IST, expect milk +0.9L/cow.

live

🩸 Blood

Anaemia · Zoonosis

Microfluidic VQC

Reco · Anaemia probability 0.71 — schedule iron-cobalt bolus within 48h.

live

🌾 Fodder

Biomass · CP%

NDVI · EVI · LSWI

Reco · Cut Plot-B silage Tuesday; CP% peaks at 14.6 before lignification.

live

🧪 Fertiliser

N·P·K · Bio

Zn-Met · Cu-Prot · Se

Reco · Replace 22% urea with chelated Zn-Met blend; cost -₹480/acre.

live

🚜 Farm

Yield · Margin

Digital twin ledger

Reco · Sell 38% of cotton harvest at Akola mandi Friday — price arbitrage.

Explainability · Risk Math

The exact formula behind every node directive

Hover or tap a card for driver definitions and the risk threshold the QSAN collapses on.

soil

Soil OC% Loss Vector

ΔOC = (Cin − k·OCt) · Δt

water

Reference ET₀ (FAO-56)

ET₀ = (0.408Δ(Rn−G)+γ·900/(T+273)·u₂(es−eᵃ)) / (Δ+γ(1+0.34u₂))

climate

Growing Degree Days

GDD = Σ max((Tmax+Tmin)/2 − Tbase, 0)

livestock

Temperature-Humidity Idx

THI = 0.8·T + RH·(T−14.4) + 46.4

blood

Anaemia Probability (VQC)

P(anaemia) = σ(⟨ψ|H_dx|ψ⟩)

fodder

Crude Protein (NIR)

CP% = α + β·log(1/R_2180nm)

fertiliser

Microbiome Disease Index

MDI = Σ wᵢ·rel_abund(pathogenᵢ) / Shannon_H

farm

Per-Acre Margin

M = Yield·P_mandi − ΣCostsᵢ − OpEx

HPLC · Elution Spectrum

Interactive chromatogram with peak intelligence

Click a peak to inspect compound identity, anomaly flag, and the linked node directive the engine routes downstream.

Peak P2Anomaly

Lysine

Retention 130s · Height 82 mAU · Width 16

Peak +18% vs spec — reduce dose 12% next batch.

Linked node

🧪 Fertiliser

Replace 22% urea with chelated Zn-Met blend; cost -₹480/acre.

Spectrum → Decision

Translate every photon into a plain-language directive

FTIR · NIR · HPLC · Vision outputs fuse through QSAN and emit one sentence the farmer can act on today.

Raw signal

C=O stretch ↑ 1715 cm⁻¹, OH band wide

Key drivers

Carbonyl peak +14%Humic OH broadeningSoil OC% drop −0.21

Do this today

Add 220 kg/ha gypsum + composted residue today.

Live confidence · fused with feed

86%

Confidence & Risk Breakdown · per-layer contributions

Σ weights = 1.00 · live · t=0

L1✓

L1 Ingest qualityw=0.10 × s=0.87

+8.7

L2✓

L2 Feature integrityw=0.15 × s=0.83

+12.4

L3✓

L3 Quantum fidelityw=0.20 × s=0.95

+18.9

L4✓

L4 GraphRAG recall@5w=0.20 × s=0.92

+18.5

L5✓

L5 QSAN α-marginw=0.20 × s=0.92

+18.3

L6✓

L6 Delivery ackw=0.10 × s=0.96

+9.6

GR✓

Safety guardrailw=0.05 × s=1.00

+5.0

Guardrails passed

✓ PII-redact✓ Dose-cap clamp✓ Stage-window OK✓ Vet-escalate✓ Source citation

Residual risks

medium · NDVI-Δ trendmedium · Heat-stress (THI)low · Stage: vegetativelow · Feature drift

Multi-Modal Analytical Core

AI Infrared · Spectroscopy · Chromatography · Vision

Decoupled four-tier pipeline turning raw photons, fluids and pixels into actionable agronomic directives.

AI Infrared (FTIR)

A(ν) = −log₁₀(I(ν)/I₀(ν))

Fourier-Transform IR scans organic matter for functional carbon vectors and molecular backbones.

NIR Spectroscopy

R → A = log₁₀(1/R)

Real-time multi-spectral reflectance quantifies chelated Zn, Cu, Se and crude protein without wet chemistry.

HPLC Chromatography

1D-ResNet on elution peaks

Asymmetric Gaussian fits separate overlapping peaks for Methionine, Lysine, B-vitamins and purity audit.

Physics-Informed Vision

PINN(NDVI, LST, RGB)

Drone + Sentinel-2 fusion flags zonal stress, lesions and block density before symptoms surface.

[ PHYSICAL INPUT STREAMS ] ──► [ Fourier-Transform FTIR Engine  ] ──► Active Carbon Vectors
[ LIQUID MICROFLUIDICS   ] ──► [ 1D-ResNet Chromatography Net   ] ──► Purity Verification
[ SPECTROSCOPIC OPTICS   ] ──► [ Multi-Spectral Matrix Solvers  ] ──► Mineral Quantulations
[ IMAGING DATA CUBES     ] ──► [ Physics-Informed Vision Model  ] ──► Zonal Stress Fields

Live Cognition · Telemetry

AI & Quantum Telemetry

streaming · 184000 pkts/min

🧠

SLM · GraphRAG · Voice

on-device · Marathi/Hindi/EN

HEALTHY

Tokens/sec

184

SLM latency

312 ms

RAG recall@5

91.4%

Graph hops

3.2

Guardrail pass

99.20%

Voice WER

7.7%

Inference throughputconf 87.4%

model QwenSLM-1.8Bctx 32kquant Q4_K_M

⚛️

QSAN · VQE · Quantum Kernel

12 logical qubits · simulator

COHERENT

Gate fidelity

97.60%

Coherence T₂

118 µs

Gate error

0.42%

VQE ⟨H⟩

-1.1370 Ha

QSAN collapse

34.0 ms

Entanglement

0.78

Amplitude |ψ|² envelope8192 shots

Circuit activity · q×step

🔗

Hybrid Fusion · Classical ⇌ Quantum

SLM logits re-weighted by QSAN amplitudes · GraphRAG retrieves quantum-kernel neighbours

hybrid loss 0.082κ kernel 0.74drift 0.60σSLA 99.94%

Quantified Benefits · Pilot Cohort

+28%

Yield uplift across cotton/rice/pulses pilots

-31%

Irrigation water saved via wave-collapse irrigation

-37%

Chemical pesticide cut via VQE biopesticide screen

1,750

tCO₂e abated per 10,000 acre cohort / season

<400ms

Edge decision latency on a ₹2,000 node

Indian languages spoken by the voice agent

Neural OS · Layered Architecture

L6 · ActionMarathi voice / SMS / IVR · KCC unlock · carbon-credit mint

L5 · DecisionQSAN wave-collapse → Critical / Stressed / Vital action class

L4 · ReasoningSLM + GraphRAG over AgriGraph knowledge graph

L3 · QuantumVQE biopesticide screen · QSAN attention · quantum kernel climate

L2 · FeaturesET₀ · GDD · NDVI-Δ · VWC-z · EC-trend · THI · spectral harmonics

L1 · IngestLoRa · Sentinel · IMD · rumen-bolus · blood-smear CNN

Autonomic AI · Quantum Neural OS

Live inferencing through the L1 → L6 stack

streaming · 6 pkt · 582ms

Telemetry inputs · feeding L1

crop-stage: vegetative

VWC

25.0%

ET₀

5.75mm

NDVI

0.636

NDVI-Δ

-0.060

THI

79.7

GDD

1192

LST-Δ

2.1°C

Self-heal

1 nodes rebalanced

Drift

0 features above threshold

Quorum

12 / 12 qubits coherent

Guardrail

5/5 passed

Layer detail · L3 Quantum

12-qubit microfluidic VQC; gate fidelity 0.991, T₂ coherence 78µs.

inference.route(L3) → autonomic.OK

Scenario Simulator

What-if before you commit the advisory

Adjust forecast rain, growth stage, and NDVI-Δ. See exactly how confidence, packet cadence, and the directive shift.

Forecast rain prob.35%

dry50%monsoon

Crop stageflowering

NDVI-Δ (14-day)-0.04

-0.150+0.10

Confidence

live

79%

→

simulated

80%

Δ +1.2 pts

In-flight packets

live

→

simulated

Δ +0 pkt

Packet cadence

live

582ms

→

simulated

769ms

Δ +187ms

Per-layer scenario contributions

L1 Ingest qualityw=0.10 × s=0.80

+8.0-0.7

L2 Feature integrityw=0.15 × s=0.82

+12.3-0.1

L3 Quantum fidelityw=0.20 × s=0.94

+18.8-0.1

L4 GraphRAG recall@5w=0.20 × s=0.86

+17.1-1.4

L5 QSAN α-marginw=0.20 × s=0.95

+18.9+0.6

L6 Delivery ackw=0.10 × s=0.95

+9.5-0.1

Safety guardrailw=0.05 × s=1.00

+5.0+0.0

Draft advisory · simulated

Hold today — telemetry within band.

Simulated confidence

80%

Scenario risks

medium · NDVI-Δ trendmedium · Heat-stress (THI)high · Stage: floweringlow · Waterlogging

ET₀ projected · 4.64 mm/day

VWC projected · 28.1 %

THI projected · 78.3