Today's Action · 06:14 ISTAI Confidence 98.4%

Hold irrigation.
Rain arriving in 14 hours.

Quantum state model predicts 82% probability of 12 mm precipitation. Root-zone moisture at 45 cm is sufficient for the next 24 hours.

Wait for IMD 09:00 update
Next Urea window: Friday morning
Pest risk score: 0.18 (low)

Field Health Index

0.78↑ 4.2%NDVI · Sentinel-2
NDVI heatmap of the monitored field
Precision agriculture · India

A farmer should never interpret a number.
Only — do this today.

AgriSensee fuses sub-surface sensors, Sentinel-2 imagery, IMD weather, and a quantum state crop model into one daily directive — delivered to the farmer's phone in their language.

14ms
Pipeline latency
6 wks
Pre-harvest yield visibility
11
Monetisation verticals
DPDPA
2023 compliant
Data sources

Six streams. One ground truth.

All streams are timestamped, validated for outliers, and stored in a time-series database partitioned by farm and field ID.

Sub-surface

Multi-depth soil moisture

15 cm · 45 cm · 90 cm probes capture infiltration and root-zone draw-down independently.

Sub-surface

Electrical conductivity (EC)

Nutrient depletion and over-fertilisation flagged before crop burn.

Sub-surface

Soil temperature

Stage-aware thresholds for germination, vegetative, flowering, grain-fill.

Atmospheric

Hyperlocal weather + ET₀

IMD + aggregator forecasts processed via Penman-Monteith reference evapotranspiration.

Orbital

Sentinel-2 / MODIS NDVI

Whole-field vigour mapping — not just sensor points. Per-field baseline normalisation.

Historical

Per-field yield archive

Seasonal input-output pairs build a field-specific model, not a generic crop model.

The intelligence stack

Five layers from raw signal to plain language.

Each layer can be audited, replaced, or retrained independently. The bottom layer is interpretable. The top layer is human.

  1. Layer 05v2.44

    Natural-language output

    “Your wheat needs water today. Irrigate for 45 minutes in the evening.” Translated to regional dialects.

  2. Layer 04v2.43

    Personalisation · federated learning

    Per-field models improve every season. Aggregate learning lifts the base model for every farm.

  3. Layer 03v2.42

    Recommendation engine

    Resolves conflicts (irrigate vs. rain forecast) into a single ranked action with a confidence score.

  4. Layer 02v2.41

    Predictive ML layer

    3–5 day soil-moisture trajectory, crop-stress likelihood, pest/disease outbreak probability, yield forecast.

  5. Layer 01v2.40

    Rules + threshold engine

    Fast, interpretable safety logic. The always-on baseline transparent to farmers and agronomists.

Decision flows

Five engines, one mailbox.

Every recommendation carries the inputs, the rule, and an internal confidence score — auditable end-to-end.

IrrigationRoot-zone moisture + 48h rain forecastIf moisture < stage threshold AND rain prob < 30% → irrigateIrrigate 45 min, 18:00 today
FertilisationEC trend + crop stageIf EC ↓ during vegetative → recommend N; if EC > 2.0 dS/m → haltApply 40 kg/ha urea, Friday AM
Pest & DiseaseT + RH + leaf-wetness durationLogistic risk score on (T, RH, LWD) for fungal classesRisk 0.18 — no action; re-check 48h
AnomalySpatial variance across sensor clusterIf σ(zone_i) > 2× baseline → flag drainage / leak / pest patchPipe leak suspected, NW corner
YieldHistorical inputs ↔ outputs on this fieldPer-field GBM updated each seasonProjected: 4.6 t/ha (±0.3)
The mathematics

The formulas underneath the directive.

We expose the math because the moat isn't secrecy — it's the field-validated dataset that calibrates every coefficient.

Live measurement
12.4 mm/day
Estimated evapotranspiration loss · Field MH-72-PUNE-04
Penman-Monteith

Reference evapotranspiration ET₀

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

Drives daily water demand. Combined with rain forecast to issue or hold the irrigation directive.

Remote sensing

NDVI · vegetation vigour

NDVI = (NIR − RED) / (NIR + RED) ; anomaly = (NDVI − μ_field) / σ_field

Per-field baseline normalisation prevents averaging away localised stress patches.

Quantum state model

Crop health superposition |ψ⟩

|ψ⟩ = α|healthy⟩ + β|stressed⟩ + γ|diseased⟩, |α|² + |β|² + |γ|² = 1

Ambiguous signals (e.g. NDVI dip + normal EC) are kept in superposition until a measurement (sensor + agronomist confirmation) collapses the state.

Pest probability

Logistic disease risk

P(outbreak) = σ(β₀ + β₁·T + β₂·RH + β₃·LWD + β₄·stage)

Calibrated per region. Triggers an alert before visible damage — not after.

Yield forecast

Per-field gradient boosted regressor

ŷ = Σₖ fₖ(x), fₖ ∈ ℱ; loss = Σ ℓ(y, ŷ) + Σ Ω(fₖ)

Trained on each field's historical input-output pairs. Updated continuously, fully retrained per season.

Decision arbitration

Ranked action utility

A* = argmaxₐ E[ΔYield(a) · price − cost(a)] − λ · Risk(a)

Resolves conflicts between simultaneous recommendations using an economic + risk-adjusted utility.

Pipeline

Sensor read to farmer notification — under 30 minutes.

Continuous micro-updates per field. Seasonal full retrains against actual yield. Drift monitored when farmers override.

1

Ingest

Sensor packets + IMD + Sentinel-2 + farmer logs · all timestamped, outlier-validated.

2

Process

Gap-fill, ET₀ via Penman-Monteith, NDVI normalisation, crop-stage tagging.

3

Decide

Rules + predictive models → recommendation engine ranks by urgency × confidence.

4

Deliver

Plain-language directive pushed to phone. Low-confidence flagged for agronomist review.

Data monetisation

The sensor is the door. The AI is the business.

API STATUS · NOMINAL
01

Commodity feeds

Pre-harvest yield signals weeks ahead of govt estimates.

02

Parametric insurance

Field-level risk telemetry → instant settlement.

03

Agri-input R&D

Which fertiliser regime correlates with EC trajectory?

04

AgriSensee Credit Score

Behaviour-based underwriting API for banks & NBFCs.

05

Soil carbon credits

Sensor-validated longitudinal soil data.

06

B2G dashboards

Anonymised district reports for state agri depts.

07

Contract farming

Soil-fit farmer ↔ corporate buyer matching.

08

Export traceability

Per-consignment provenance certificates.

09

Futures hedging

Lock farmer income with 6–8 wk yield visibility.

10

Intelligence Terminal

Bloomberg-style institutional SaaS.

11

State white-label

AgriSensee engine under the state's brand.

+ COMPOUND

Every revenue stream is funded by the data already collected for the farmer.

Indian farmer holding fertile soil
Data ownership & consent

The farmer owns the raw data. We earn the licence.

DPDPA 2023 compliant. Individually identifiable farm data is never sold — only anonymised, sub-district aggregates power institutional products.

  • Geographic blurring

    Coordinates rolled up to mandal level for any external feed.

  • Identity stripping

    Names, IDs, device IDs replaced with anonymised cohort tags.

  • Voice-guided consent

    Plain-language ToS in regional languages, voice walkthrough for low-literacy users.

  • Re-identification ban

    Every Data Licensing Agreement triggers automatic termination on breach.

DPDPA 2023ISO 27001NABARD-aligned