Soil health · AI · IoT · Agtech
TerraSensus
Soil health is invisible until it's catastrophic. TerraSensus makes it continuous — sensor monitoring, AI crop recommendations, and instant critical alerts for farmers who can't afford to be wrong.
🌱 The Problem
30–50%
of applied nitrogen never reaches the crop — leaches into groundwater or volatilises as N₂O
265×
more potent than CO₂ — nitrous oxide from over-fertilisation accounts for 6% of global greenhouse gas emissions
$12,500
average annual savings on a 200 ha farm through precision application — from a $50,000 fertiliser budget
🪴 Most farmers apply fertiliser by habit, by last season's results, or by blanket agronomic guidelines — not by what is actually in their soil right now. Soil composition changes with weather, crop rotation, microbial activity, and drainage. A field that needed heavy nitrogen last autumn may have adequate levels this spring.
♻️ The ecological cost is externalised — paid by rivers, insects, and future generations. Agricultural runoff is a leading cause of freshwater dead zones. TerraSensus makes that cost visible and attributable, season by season, field by field.
⚠️ The harder problem: one-size fertiliser rules don't work. Every crop has a different relationship with nitrogen, salinity, and pH — what looks like a deficiency on a generic chart is a perfectly healthy reading for the right variety. A system that fires the same alerts regardless of what's growing is worse than no system at all. Three real plots illustrate why, below.
🗺️ Three Plots, Three Realities
Mykola
Kherson Oblast, Ukraine
Watermelon (GI protected) · Sandy chernozem · Continental
Watermelons need high potassium to develop fruit — but too much nitrogen and the plant puts all its energy into leaves instead. Generic fertiliser guidelines would over-apply nitrogen here and ruin the crop. Kherson watermelons are also culturally significant — they became a symbol of Ukrainian resilience during the occupation.
Fatima
Ferghana Valley, Uzbekistan
Cotton · Arid desert, saline irrigation · Arid
Cotton naturally tolerates high salt levels in the soil — levels that would kill most other crops. So when TerraSensus reads high salinity on Fatima's field, it's not an emergency. A generic alert system would fire a warning here every single day, on a perfectly healthy field.
Elena
Willamette Valley, Oregon
Pinot Noir · Volcanic Jory loam · Maritime
Great wine grapes actually need low nitrogen — too much and the vine grows lush leaves instead of concentrating flavour into the fruit. Elena's soil readings look like a deficiency on any standard chart. They're not. This is exactly what a healthy Pinot Noir vineyard looks like.
🤖 AI Usage Policy
🚨 AI never touches a critical alert
Rule-based engine only — synchronous, local, zero network. A farmer in a drought cannot wait for an API call.
🧾 AI never touches financial calculations
ROI, savings, spend totals are deterministic SQL/Python. Numbers affecting livelihoods are not estimated.
🔍 Every AI response shows its source
Model name, agronomist disclaimer, flag button — visible on every recommendation. TerraSensus is a decision support tool, not a decision maker.
🌿 Crop-aware thresholds, not global defaults
Pinot Noir runs intentionally low N. Cotton tolerates high EC. Watermelon needs high K. Global defaults create false alarms on healthy fields.
🏗️ Stack
📱 Mobile
React Native (Expo)
iOS-first, farmers in the field
🌐 Web
Next.js
Admin dashboard, analytics
⚙️ Backend
FastAPI (Python)
4 microservices on Cloud Run
📡 Queue
GCP Pub/Sub
Sensor telemetry pipeline
🗄️ Operational DB
Cloud SQL (PostgreSQL)
Sensor readings, logs, plots
📊 Analytics DB
BigQuery + dbt
ELT via Datastream, mart tables
🤖 AI
Vertex AI (Gemini Pro)
Fallback: Claude Sonnet → rule-based
📄 Documents
Google Document AI
Lab report parsing + Gemini Vision fallback
🔔 Alerts
Firebase Cloud Messaging
Push to mobile, rule-based only — no AI
🏗️ Infra
Terraform
Cloud Run, Cloud SQL, BigQuery, Pub/Sub, GCS — all provisioned as code
📍 Status
Cover artwork via ArtStation