⚛ Quantum-native🧠 SLM · GraphRAG📐 Physics-informed🛰 Sentinel-2 · IoT · LoRa

The full AgriSensee intelligence stack — from soil photons to spin Hamiltonians.

Interactive simulators for every layer of the platform: ingestion pipelines, agronomic physics (FAO-56, GDD, NDVI/EVI/LSWI), CNN-LSTM yield prediction, Neo4j + pgvector GraphRAG, edge-quantized SLM advisory, and quantum QAOA / VQE optimization.

§1 · Architecture

End-to-end data pipelines

                                      [ IoT Edge Nodes (ESP32 / LoRa) ]
                                                      │
                                             (LoRaWAN Gateway · SIM7600)
                                                      │
                                               [ MQTT Broker ]
                                                      │
                                              [ Apache Kafka ]
               ┌──────────────────────────────────────┴──────────────────────────────────────┐
               ▼                                      ▼                                      ▼
    [ Flink Stream Processor ]               [ Celery Workers ]                    [ PySpark Core Ingest ]
    (15-min windowing · anomaly)             (mobile media · voice STT)            (Sentinel-2 STAC · IMD)
               │                                      │                                      │
               ▼                                      ▼                                      ▼
       [ TimescaleDB · hot ]                  [ S3 · raw media ]                  [ Iceberg · warm lakehouse ]
               │                                      │                                      │
               └──────────────────────────────────────┼──────────────────────────────────────┘
                                                      ▼
                                      [ Unified Processing Layer ]
                                   (vector embeddings · graph synthesis)
                                                      │
                                        ┌─────────────┴─────────────┐
                                        ▼                           ▼
                                   [ pgvector · HNSW ]         [ Neo4j property graph ]

🌡 Hot tier

TimescaleDB hypertables indexed by (ts, sensor_node_id, field_id). Sub-second range scans across 6 months of 15-min telemetry.

🧊 Warm tier

Apache Iceberg on S3. Nightly PySpark batches snapshot seasonal economics, NDVI trends, and yield-feature tables.

🗄 Cold tier

WORM compressed bundles for consent timestamps, governance delta logs, and KYC audit trails. AES-256-GCM at rest.

§2 · Security · DQ

Tokenization, ZKP & Data Quality score

HMAC-SHA256 · format-preserving mask

Raw KYC identifier

downstream → ANON-ID-AD0BCEF9

Pepper held in HSM. Raw never persists past the API gateway. Subsidy eligibility uses Groth16 NIZKPs over BN254 — proof without decryption.

Data-Quality score · DQₛ

DQₛ = 0.5·C_fields + 0.2·I_spatial + 0.3·T_freshness

C_fields · mandatory completeness0.92

I_spatial · polygon valid (0/1)1

T_freshness · telemetry variance0.78

/ 100 · used as ML sample weight

§3 · Mathematical foundations

Physics-informed agronomic formulas

FAO-56 Penman-Monteith

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

T · air temperature28.00 °C

RH · relative humidity55 %

u₂ · wind @ 2 m2.10 m/s

Rn · net radiation15.00 MJ/m²/d

G · soil heat flux0.50 MJ/m²/d

Reference ET₀

6.02 mm/day

Growing Degree Days · GDD

GDD = Σ max((Tmax+Tmin)/2 − Tbase, 0)

Tmax (daily)32.00 °C

Tmin (daily)18.00 °C

Tbase (crop)10.00 °C

Season days60 d

Daily

15.0

Cumulative

900

Sentinel-2 spectral indices

NDVI · EVI · LSWI from top-of-atmosphere reflectance

B02 · Blue0.08

B04 · Red0.11

B8A · NIR0.42

B11 · SWIR0.21

NDVI

0.585

EVI

0.524

LSWI

0.333

§4 · Data science

CNN-LSTM yield model · MobileNetV3 edge classifier

Hybrid yield prediction graph

Sentinel-2 COG stack  ─►  [ 2D CNN extractor ]  ─┐
                                                  ├─►  [ concat ] ─► [ BiLSTM ] ─► [ Dense ] ─► ŷ (t/ha)
15-min soil & weather ─►  [ 1D aggregator     ]  ─┘

2D-CNN
3×3 · 64ch

BiLSTM
128 hidden

Dense
→ ŷ

Trained with DQₛ sample weights · WGAN-GP augments rare disease & extreme weather edge-cases.

Edge pest classifier · MobileNetV3-Small

class AgriSenseEdgeClassifier(nn.Module):
  def __init__(self, num_classes=45):
    super().__init__()
    self.backbone = mobilenet_v3_small(pretrained=True).features
    self.pool = nn.AdaptiveAvgPool2d(1)
    self.head = nn.Sequential(
      nn.Linear(576, 128), nn.Hardswish(),
      nn.Dropout(0.2), nn.Linear(128, num_classes))
  def forward(self, x):
    x = self.pool(self.backbone(x))
    return self.head(torch.flatten(x, 1))

2.5 M params

INT8 · 1.8 MB

~22 ms / frame

§5 · GraphRAG

Neo4j property graph + pgvector HNSW retrieval

Property graph schema

(:Farmer {id})─[:OWNS]──►(:Field {id, area_ha})
        │                    │
        │                    ├─[:HAS_SOIL]──►(:SoilProfile)
        │                    ├─[:LOCATED_IN]►(:Village)
        │                    └─[:CURRENT_SEASON]►(:CropCycle {season:'Rabi'})
        │                                          │
        │                                          └─[:LOGGED_PEST]►(:PestOutbreak)
        └─[:SELLS_AT]─►(:Mandi)─[:PRICE_TICK]►(:PriceQuote)

Cypher example:

MATCH (f:Farmer)-[:OWNS]->(field:Field)-[:HAS_SOIL]->(s:SoilProfile {texture:'loam'})
MATCH (field)-[:CURRENT_SEASON]->(c:CropCycle {crop:'onion'})
MATCH (c)-[:LOGGED_PEST]->(p:PestOutbreak {symptom:'aphid'})
RETURN f.id, field.id, p.severity ORDER BY p.severity DESC

pgvector · cosine ANN search

embedding <=> query · HNSW · top-K

kb-103cos_d = 0.400

Loamy soils in the Yeola block (Nashik) retain 0.18–0.22 cm³/cm³ AWC; irrigation triggers at MAD 50 %.

#soil#loam#irrigation

kb-021cos_d = 0.800

Aphid colonies on Allium spp. peak at 28–32 °C with RH > 60 %. Treat with neem oil 5 ml/L or imidacloprid 17.8 % SL @ 0.3 ml/L.

#onion#pest#aphid

kb-044cos_d = 0.800

Stemphylium leaf blight presents as elongated tan lesions on onion leaves; rotate mancozeb 75 % WP and tebuconazole.

#onion#disease#blight

kb-067cos_d = 0.800

Lasalgaon mandi historical: Bhima Super grade-A peaks ₹2,400/q in Dec–Feb post-Rabi harvest.

#onion#mandi#price

§6 · Edge SLM · GraphRAG

Phi-3-mini · 4-bit NF4 · offline advisory

Field inputs

CropSymptom / question

Soil moisture42 %

Air temperature28.00 °C

NDVI drop (10 d)15 %

3.8 B paramsAWQ NF4ONNX Mobileoffline

Compiled prompt → local generation

[System: Professional Agronomist Advisory · Phi-3-mini · NF4 · ONNX Mobile]
Context:
  - Location: Yeola, Nashik, Maharashtra
  - Crop: Onion · Bhima Super
  - Soil moisture: 42% · Temp: 28°C
  - Sentinel-2 NDVI drop: 15% (10 d)
  - Graph: high RH last 72h · aphid outbreak nodes within 3 km
  - Vector hits: kb-021 (aphids), kb-044 (Stemphylium blight)

User: "leaves curling"

advisory · marathi/english

• Likely thrips/aphid stress + early Stemphylium under high RH.
• Moisture in optimal band — no irrigation action.
• Hold harvest decision — re-check NDVI in 5 days.

§7 · Quantum horizons

QAOA scheduling · VQE soil chemistry

QAOA · harvester scheduling

H_C = Σ αᵢ Ẑᵢ + Σ βᵢⱼ ẐᵢẐⱼ · αᵢ ∝ (1−NDVIᵢ)

harvester capacity7.00 ha

γ · priority weight0.60

β · transport weight0.40

field	NDVI	dist	area	picked
F-01	0.42	1.2km	1.8ha	○ 0
F-02	0.71	2.6km	2.4ha	○ 0
F-03	0.31	4.1km	1.1ha	● 1
F-04	0.58	3km	3.2ha	○ 0
F-05	0.22	5.8km	2ha	● 1
F-06	0.66	6.1km	1.5ha	● 1

⟨H_C⟩

-0.926

area used

4.6 ha

fields

VQE · soil-nutrient ground state

⟨H⟩_θ = ⟨ψ(θ)| H_chem |ψ(θ)⟩ · minimize over θ

Variational ansatz |ψ(θ)⟩ encodes N–P–K orbital occupation under varying moisture. Ground-state energy feeds the downstream fertilizer recommender — replacing regional lookup tables with site-specific chemistry.

BN254 / Groth16QAOA p=2VQE · UCCSDQiskit · PennyLane