We build custom software, hardware and data analytics for universities, research centres and agricultural companies. Sensing, IoT platforms, digital twins, computer vision and applications for thousands of users.
We turn a hypothesis into a system that runs on a real farm: sensors installed, data flowing and an app in the hands of the people who use it.
We design the datalogger, write the backend, integrate the agronomic model and ship the app. No link is subcontracted.
Every project is designed to produce data that serves farm management and a peer-reviewed paper at the same time.
Who it's for
For groups that need to instrument a trial, set up a sensor network or turn a model into a tool people use in the field.
For cooperatives, advisors, industry and manufacturers that need a tool that doesn't exist or doesn't fit what's on the market.
What we build
These are the capabilities we have put into production on real projects. Each card says where.
Our own devices on Arduino MKR with a custom PCB, solar autonomy and support for LoRaWAN, SigFox, NB-IoT, GSM, Wi-Fi and Bluetooth. They read SDI-12, RS-485, I2C, SPI and analogue sensors.
Open IoT platform · Agronomy 2022
FIWARE backend with Orion Context Broker (NGSI-LD), IoT Agents and QuantumLeap. Docker deployment, edge computing on Raspberry Pi and a standard API to interoperate with other platforms.
Open IoT platform · University of Córdoba
FAO-56 dual-coefficient water balance, wet-bulb models for drip irrigation and an AquaCrop digital twin (py-aquacrop) fed by sensors and weather forecasts. Irrigation planned 7 to 15 days ahead.
IMIS · Computers and Electronics in Agriculture 2025
Convolutional neural networks that estimate canopy cover from a phone photo, with in-field segmentation and model calibration.
Smart Agricultural Technology 2025
React Native apps with a local-first architecture: they work without coverage and sync when it returns. React web interfaces with a farm → plot → device hierarchy, per-user roles and permissions and a private API per farm.
IMIS · IoT platform · Finquet
Connection to RainDancer GPS irrigation systems and Briggs irrigators, Davis stations, LI-COR 710 evapotranspiration sensors, weather APIs and, in Finquet, the field book and management systems.
IMIS · PDM Produce
Projects
With Cranfield University · PDM Produce (UK) Ltd · University of Córdoba · RSPAWIR programme
Lettuce, celery and potato in Shropshire, with over-abstracted catchments, restrictive abstraction licences and drought summers in 2022, 2023 and 2025. The farm had to irrigate less without losing quality or yield.
A three-tier system: a sensor network (weather stations, LI-COR 710, root-zone soil moisture) connected through IoT agents; a Node.js core that integrates the AquaCrop model with real-time data and forecasts; and a local-first mobile app to manage plots, compare irrigation scenarios and calibrate the model with crop photos. Integrated with the farm's RainDancer and Briggs irrigation systems.
With Department of Agronomy, University of Córdoba · María de Maeztu Unit of Excellence
Commercial IoT platforms were closed, expensive and had no standard API: no third-party sensors and no way to multiply measurement points. With forecasts of up to 40% less rainfall in parts of Andalusia, an open alternative was needed.
Open-source multi-protocol dataloggers with a solar panel, a FIWARE backend with the FAO-56 water balance and wet-bulb model as a microservice, and a React web interface. Deployed on a 42 ha Arbequina and Arbosana farm under 60% regulated deficit irrigation with a 7-day forecast.
Published research
Finquet's technology comes out of precision-irrigation, IoT and remote-sensing research. These are the team's peer-reviewed papers.
Write to us with the problem, the crop and the timeline. We'll tell you honestly whether we can help and how we'd approach it.