INDUSTRIES / AGRICULTURE TECHNOLOGY
AGRICULTURE TECHNOLOGY.
Field systems for agricultural operators working beyond reliable connectivity, on mid-range hardware, where offline capability is an architecture rather than a feature.
- SECTOR
- 04 of 06
- DEFINING CONSTRAINT
- Connectivity & hardware
- DISCIPLINES APPLIED
- Applied AI · Digital Products
- DOCUMENTED WORK
- 1 engagement
02 / THE CONSTRAINT — CONNECTIVITY & HARDWARE
THERE IS NONETWORK OUT THERE.
Agricultural software is used standing in a field, on a mid-range phone, by someone wearing gloves, in bright sun, with no signal and no opportunity to troubleshoot. Every assumption that holds in an office fails there.
Offline capability cannot be added to a product built on the expectation of connectivity. The data model, the conflict resolution and the sync boundaries all have to be designed for it from the start.
The same applies to inference. A model that requires a round trip to a server is not available at the moment of use, which means it has to run on the device, within its memory and battery budget.
03 / What it means in code
FOUR DECISIONS.
OFFLINE FIRST
Local database as the source of truth; sync as a protocol with explicit conflict resolution.
ON-DEVICE INFERENCE
TensorFlow Lite models quantised against real mid-range device profiles.
FIELD INTERFACE
Large targets, high contrast, and operation with one gloved hand.
BATTERY BUDGET
Inference and sync scheduled against a full working day away from power.
04 / DISCIPLINES APPLIED
Applied AI · Digital Products
Most engagements in this sector draw on more than one discipline. The combination is decided by the constraint above rather than by a service menu.
05 / Selected work
PROOF.
OFFLINE
Machine vision that works where there is no signal
AN AGRICULTURAL TECHNOLOGY OPERATOR IN LATIN AMERICA
TensorFlow Lite · On-device inference · React Native
All client work is anonymised by sector and geography. Named references are available under NDA during a live engagement discussion.