About AUTOFARM™

AUTOFARM™ is a sovereign agricultural intelligence system designed to understand how a crop grows in a given environment — and to optimize the only part of agriculture humans can truly control:
parcel‑level management

It transforms agronomic cycles into machine‑interpretable intelligence, enabling systems that learn, reason, and improve crop performance cycle after cycle.

Mission

To build a global cognitive protocol that captures:

how a crop reacts to its environment,
how management decisions influence yield and nutritive value,
how stress (water, heat, nutrients, disease) impacts performance,
how to determine the best possible intervention at any moment.

AUTOFARM™ focuses on the 40–60% of yield that depends on gestion parcellaire, not on uncontrollable factors like weather.

Vision

To create a unified agricultural intelligence layer that:

understands crop physiology,
quantifies stress through indicators like evapotranspiration (ET),
learns from real cycles,
recommends optimal responses,
improves yield and nutritive value over time,
remains sovereign, explainable, and transparent.

This intelligence layer supports:

open‑field farms
greenhouses
controlled environments
research infrastructures
autonomous farming units

Architectural Foundations

AUTOFARM™ is built on three integrated components:

1. Cognitive Protocols

Machine‑readable rules describing crop behavior, stress responses, and causal dependencies.

2. Causal Crop Models

Explainable models that learn from real cycles and determine the best management response to non‑controllable environmental parameters.

3. CIF — Cycle Invariant File

A universal schema capturing the full agronomic cycle, including:
- environment
- management
- stress
- plant reaction
- yield losses
- nutritive value

The CIF is the memory that powers the cognitive core.

Why Sovereignty Matters

Agriculture is increasingly dependent on opaque, proprietary systems.
AUTOFARM™ takes the opposite path:

transparent logic
open standards
explainable causality
protocol‑level governance
independence from black‑box AI

This ensures agricultural intelligence remains trustworthy, auditable, and aligned with national food security.

Supported by Innosuisse

Beneficiary of an Innosuisse Mentoring Voucher (Swiss Innovation Agency).
Project N° 136.046 IMIA‑ENG — Codename: CompaniX.

This support reinforces AUTOFARM™’s institutional credibility, research alignment, and strategic relevance.