AIOTF · Neural OS Core
Live · 14 ms target latency

AgriSense Climate Neural OS · Quantum Attention Core

Submit a multi-sensor payload and watch it flow through six isolated tiers — Ingest, Features, Quantum, Reasoning, Decision, Action — with QSAN wave-collapse classifying the field as Vital, Stressed, or Critical.

L1 · Ingest payload

Soil VWC (%)32.00
Soil temp (°C)24.00
EC (mS/cm)1.40
NDVI0.72
ΔNDVI / week0.02
ET₀ (mm/day)4.10
GDD cumulative612
VPD (kPa)1.10
Rumen bolus (°C)38.60
Sentinel-2 age (h)6

L5/L6 · Collapsed decision

awaiting packet
—

Submit a packet to collapse |ψ(t)⟩.

P(Vital)
0%
P(Stressed)
0%
P(Critical)
0%

Six-layer execution trace

idle
L1
Ingest
LoRa · Sentinel-2 COG · IMD · Rumen-bolus
—
awaiting submission
—
IDLE
L2
Features
ET₀ · GDD · NDVI-Δ · VWC-z · spectral harmonics
—
awaiting submission
—
IDLE
L3
Quantum
QSAN attention · VQE biopesticide screening
—
awaiting submission
—
IDLE
L4
Reasoning
Local SLM + GraphRAG over AgriGraph
—
awaiting submission
—
IDLE
L5
Decision
QSAN wave-collapse · |ψ⟩ → class
—
awaiting submission
—
IDLE
L6
Action
Voice/SMS · KCC unlock · Carbon mint
—
awaiting submission
—
IDLE

QSAN math

|ψ(t)⟩ = α·e^{iω₁t}|Vital⟩
       + β·e^{iω₂t}|Stressed⟩
       + γ·e^{iω₃t}|Critical⟩

H = λ_moist·Ẑ_VWC + λ_canopy·X̂_NDVI
    + Σₖ Jₖ·Ẑₖ·Ẑ_{k+1}

λ_moist = 0.00   λ_canopy = 0.00
α
0.000
|·|² = 0.000
β
0.000
|·|² = 0.000
γ
0.000
|·|² = 0.000

PostGIS · TimescaleDB write

-- awaiting commit --

Geo-sharded into node_cluster_nashik_yeola_04 · GIST spatial index on plot polygon · DPDPA tokenized identity.

Recent collapses

No runs yet.

Farmer Solution · Deep Dive

Visualise risk · Recommend biologicals · Learn from outcomes

An AI-enabled companion that shows the farmer what threatens this crop today, prescribes the right bio-product at the right window, and closes the loop by feeding season outcomes back into next year's model.

1 · Problem

Farmers cannot see the risks stacking against them

  • · Climate volatility: erratic rainfall, heat waves, shifting monsoon windows.
  • · Soil degradation: ~30% of Indian soils are OC-poor, salinity creeping past EC 2 mS/cm.
  • · Disease pressure: 20–40% yield loss from fungi, bacteria, pests every season.
  • · Product confusion: hundreds of biologicals, no field-specific guidance on which and when.
  • · No feedback: outcomes are never captured — the same mistakes repeat every year.
2 · Solution prototype

A 3-pillar closed loop, powered by the Neural OS

S
See
Risk heat-map of climate, soil, disease per field
A
Act
Biological product + timing tailored to crop stage
L
Learn
Outcomes flow back to retrain the model
closed loop
3a · Awareness

Live risk dashboard for this field

field FLD-20260621-0042
WaterLOW
0
HeatLOW
0
DiseaseMED
55
SoilLOW
0
NutrientLOW
0

Scores fuse live sensor payload (VWC, EC, T°, VPD), satellite NDVI Δ, and phenology GDD — recomputed on every submit above.

3b · Recommendation

Best-fit biological products for today's field state

Pseudomonas fluorescens (2×10⁹ CFU/ml)
MED
Foliar biocontrol
Window · Spray at dawn, next 24h before dew break
Leaf wetness + T 24°C favour fungal pressure
3b · Drill-down

Why this recommendation — step by step

  1. STEP 1
    Signals collected
    T 24.0°CVPD 1.10ET₀ 4.1VWC 32%EC 1.40NDVI 0.72Δ 0.020GDD 612Sat age 6.0h
  2. STEP 2
    Risks derived from signals
    water 0heat 0disease 55soil 0nutrient 0
  3. STEP 3
    AgriGraph match → Pseudomonas fluorescens (2×10⁹ CFU/ml)
    Leaf wetness + T 24°C favour fungal pressure · window: Spray at dawn, next 24h before dew break
  4. STEP 4
    Evidence weighting
    Leaf wetness proxy (VPD low) (1.10 kPa)w=0.35 · s=0.00 · +0
    Canopy T° in fungal band (24.0 °C)w=0.30 · s=0.90 · +27
    Disease risk score (55)w=0.25 · s=0.55 · +14
    NDVI Δ decline (0.020)w=0.10 · s=0.00 · +0
  5. STEP 5
    Guardrails checked
    • Rain forecast window ≥ 6 h dry— no precip expected in next 6 h
    • Wind < 12 km/h at application— VPD 1.10 kPa proxy
    • Crop stage compatible— GDD 612 °C·d in window
    • No conflicting agrochemical in 48 h— AgriGraph edge check
  6. STEP 6
    QSAN collapse & delivery
    |α|² collapsed to — in — · deliver via L6.
Confidence
52%

Score fuses evidence weight (41 pts) + QSAN state bonus. Below 55% the system defers to a human agronomist instead of auto-delivering the spray schedule.

Priority · MED
Window · Spray at dawn, next 24h before dew break
Agent class · Foliar biocontrol
3c · Application calendar

14-day biological rx schedule · field FLD-20260621-0042

GDD 612 · stage cursor
Product · dose
Mo
17
Tu
18
We
19
Th
20
Fr
21
Sa
22
Su
23
Mo
24
Tu
25
We
26
Th
27
Fr
28
Sa
29
Su
30
Foliar biocontrol
2.5 ml / L, 200 L / acre
APPLY
APPLY
Pseudomonas fluorescens (2×10⁹ CFU/ml)
MED
Apply on · Tue, Aug 18
Dose · 2.5 ml / L, 200 L / acre
Volume · 500 ml
Method · Foliar spray at dawn
Cadence · Repeat every 10 d until risk < 40
Window: Spray at dawn, next 24h before dew break
3c · Feedback loop

Season outcomes train next year's recommendation

0 runs logged
Season telemetry — data quality & collapse state over recent runs
Submit a payload above to start logging outcomes.
vitalstressedcritical
Field actionOutcomeModel updateNext rx
Every applied product + season yield is logged → GraphRAG updates edge weights → next year's recommender starts smarter for this field.
4 · Decision-making process

How Neural OS turns raw signals into a farmer directive

  1. Step 1
    L1 Ingest
    Sensor · NDVI · weather → tokenised
  2. Step 2
    L2 Validate
    DPDPA + DQ checks reject bad rows
  3. Step 3
    L3 Fuse
    Multi-modal alignment + risk scoring
  4. Step 4
    L4 QSAN
    |ψ⟩ wave-collapse over state basis
  5. Step 5
    L5→L6 Act
    Biological rx + voice / SMS delivery
Every recommendation is grounded in AgriGraph (verified soil × crop × product edges) and gated by the quantum attention core — so the farmer sees only actions with |α|² > 0.55 collapse probability. If the model is uncertain, it defers to a human agronomist instead of hallucinating a spray schedule.
Sub-14ms interoperability
Six decoupled tiers process multi-source streams inside an optimized 14ms window.
Predictive crop stress
Wave-pattern modeling surfaces sub-surface anomalies weeks before canopy degradation.
Hallucination-safe advisories
SLM inference bound to AgriGraph + verified soil baselines.
Built-in DPDPA governance
Tokenization & consent checks embedded at the ingest boundary.