Technology

A closed-loop system for sensing, deciding, and acting at the leaf level.

AgroMind is a vertically-integrated robotics stack: insect-scale hardware operating inside the canopy, on-device perception, swarm coordination, a live digital twin, and a cloud layer that learns across plots and seasons.

Architecture · Overview

Sense → Think → Act, continuously, autonomously.

Every robot runs the full loop on-device. The fleet coordinates as a swarm. The cloud trains the next generation of models on what the fleet learns.

01 · SENSE

Multi-modal perception inside the canopy

Macro-resolution cameras, environmental sensors and proximity awareness, captured from positions no aerial or ground tool can reach. Continuous streams, not snapshots.

Vision Spectral Microclimate
02 · THINK

On-device AI with cloud-trained models

Edge inference for real-time decisions inside the canopy. Cloud training pipelines for new pests, diseases and crops. A digital twin keeps every plot in sync.

Edge AI Digital twin Continuous learning
03 · ACT

Targeted micro-treatment, verified in situ

Precise micro-dosing at the infection site, followed by a verification scan to confirm suppression. The action closes the loop the rest of the field can't see.

Micro-dosing Verification Closed loop
The stack

One integrated system, top to bottom.

Each layer is built to work with the others — not as a marketplace of point tools, but as a single robotics platform engineered around one problem.

Layer 01 · Hardware
AGRO-SCOUT

Insect-scale robot built for canopy operation. Designed for accessibly-priced serial production, with a payload tuned for micro-dosing and on-board perception.

Form factor: insect-scale In-canopy mobility Onboard sensors Treatment payload
Layer 02 · Compute
Edge AI

On-device inference for detection, decision and treatment verification. Runs without permanent cloud connection, so the system works deep inside the canopy and far from connectivity.

CV pipeline Low-latency inference Offline-tolerant
Layer 03 · Coordination
Swarm Engine

Coordinates multiple robots inside the same plot — task allocation, area coverage, redundancy and shared situational awareness. Built for graceful degradation when units drop or reposition.

Multi-agent Task allocation Mesh-aware
Layer 04 · Field operating system
AgroMind OS

The runtime layer the swarm runs on. Mission management, telemetry, secure update channels, and an API surface for integrations with downstream systems.

ROS2-based OTA updates Telemetry Open API (planned)
Layer 05 · Twin
Digital Twin

A live, plant-level model of the plot. Risk zones, treatment history, microclimate, and disease progression all flow into one structure that decisions and reports read from.

Plant-level resolution Time-series Geospatial
Layer 06 · Cloud
AgroMind Cloud

Model training, fleet management, cross-plot analytics and external integrations. The cloud doesn't make field decisions in real time — but it's where the system learns.

Model training Fleet ops Analytics
Precision micro-treatment of coffee cluster
Technological moat

Defensibility comes from the integration, not any one part.

The hard problem isn't building a camera, a robot, or a model — it's making all three operate reliably inside a real canopy, at a unit cost that growers can absorb, with treatment outcomes that hold up across seasons. Our patent strategy is in development across four areas:

  • 01 Robotic hardware. Form factor, in-canopy mobility and serial-production architecture.
  • 02 Control capsule. Field-deployable compute, comms and charging unit that anchors the swarm.
  • 03 Detection & treatment algorithms. Early-stage biological identification and micro-dosing decision logic.
  • 04 Grower application. The interface and data layer between the swarm and the grower.

Patents pending across the four areas above.

MVP scope · 2026

What we're building first.

The MVP is intentionally narrow. The goal isn't the full platform — it's a closed-loop demonstration on a single pest, in a controlled plot, with technology choices we can scale from.

MVP · 01

1–3 robots in-field

A small, controlled swarm operating in a real coffee plot — enough to validate coordination, treatment and verification end-to-end.

MVP · 02

Vision AI on a single pest

Initial models trained on one of the three coffee use cases. Detection-first, then treatment, then closed-loop verification.

MVP · 03

ROS2 + simple dashboard

Standard, open robotics middleware for the runtime. A minimal grower dashboard to make the system legible from day one.

MVP · 04

Control capsule v0

A field-deployable compute, comms and charging anchor. The smallest viable unit that lets a swarm run continuously in a plot.

For engineers

Open problems we're hiring around.

If any of these feel like the problem you want to spend the next few years on, we'd like to talk.

1 · Canopy locomotion at insect scale

How does a small robot move inside dense, irregular foliage — finding stable footholds on branches, navigating across leaves, and operating reliably across coffee, vine and orchard canopies?

2 · Early-stage biological detection

How do we identify diseases and pests before they're visually obvious — using vision, spectral signals, and environmental context, with limited initial training data and high false-negative cost?

3 · Micro-dosing & treatment verification

Precise on-target delivery of active material at the leaf or cluster level, followed by an in-situ verification scan that closes the treatment loop. Mechanical, fluidics, and perception all interact here.

4 · Robust swarm behavior

Coordinating a small swarm inside a plot with intermittent connectivity, partial information, and units that may drop or reposition. Graceful degradation matters more than raw performance.

5 · Unit economics that work for growers

Form factor, materials, manufacturing and service model all combine into the question that determines whether this scales: can a grower afford to deploy this — and what does the cost curve look like at volume?

Talk to us about building this
Developer portal

Docs, SDK and APIs arrive with the MVP.

Once the first robots are operating in-field, we'll publish the API surface, integration guides, and the parts of the stack that make sense to open up. If you'd like early access — or to influence what gets opened — get in touch.

Request early access Investor brief
Coming · 2026

Currently in development:
— Hardware reference architecture
— Edge AI inference SDK
— Mission control API
— Digital twin schema