Develop, validate and scale AI/data research into deployable agritech workflows

Partner with Crop Urbanis from hypothesis and lab trials through pilot validation, agronomic ground truth and commercial de-risking.

The research programme connects controlled-environment agriculture, structured datasets, computer vision, sensing, model evaluation and expert-reviewed decision support.

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The projects listed below are active and planned research initiatives led by Crop Urbanis to develop, validate and translate agritech innovations into deployable solutions.

If a project interests you and you’d like to collaborate, contact us to discuss partnership formats, pilot funding and next steps.

AI/data decision-support research

Crop Urbanis is building a practical research-to-operations bridge for controlled-environment agriculture. The objective is to convert repeated agronomic workflows into structured datasets, expert-reviewed AI outputs, KPI dashboards and cloud-based decision-support modules.

greenhouse and vertical-farm data structuring
crop protocol and SOP representation
AI-assisted agronomic knowledge retrieval
multilingual training and operating documentation
computer-vision and sensing validation
model evaluation against agronomic ground truth
MLOps workflows for pilots and partner deployments
ROI and cost-performance analysis for growers and suppliers

What we publish vs keep private

Crop Urbanis can share research summaries, methodology notes, pilot scopes, anonymised benchmarks and high-level architecture. Client-specific datasets, farm economics, supplier documents, private protocols and deployment details remain confidential unless separately agreed.

Why cloud and AI infrastructure matters

Applied AI for agriculture requires secure storage for project and trial data, compute for model evaluation, inference for knowledge retrieval and SOP generation, dashboards for operational KPIs, image/sensor processing and traceable workflows that let experts review outputs before decisions are made.

AI benchmarkingPilot validation

AI research center for controlled-environment agriculture

Evidence-driven AI for greenhouses, vertical farms and protected systems.

Crop Urbanis tests, benchmarks and adapts AI workflows for agricultural operations, including computer vision, sensing, robotics, structured datasets, agronomic ground truth and pilot validation. The goal is not to deploy generic AI tools blindly, but to measure whether they improve real crop, labour, quality, cost and reliability outcomes.
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Plant-cell systemsRegulatory pathway

"Cultured Fruit" — edible fruits from plant cells

Fruit built from plant cells, not orchards.

Growing edible fruits directly from plant cells by inducing flowering and fruiting without trees. The goal: consistent flavor and nutrition with lower land use, contingent on sustainable sugar inputs and a clear regulatory path.
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Use-case selectionHuman-review checkpoints

Implementation and adaptation of AI products for agricultural companies

From AI promise to farm-ready performance.

Crop Urbanis helps identify high-value AI use cases, adapt tools to crops and hardware, validate performance in greenhouse or field conditions, define human-review checkpoints, and operationalise successful pilots through training, dashboards, MLOps and ROI models.
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Lab-verified nutritionRecipe variables

Personalised vegetable nutrition

Grow vegetables matched to your nutritional profile.

A home CEA system and app that grows vegetables aligned to your dietary goals by tuning cultivar and recipe variables, backed by lab-verified nutrition data and optional clinical partnerships for specific patient populations.
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Microgrid modellingReliability models

Autonomous farms for remote islands

Reliable fresh produce where logistics are hardest.

Containerised, automated greenhouses for islands. Powered by microgrids, optimised for water and labour, and validated with transparent cost and reliability models so communities can replace fragile imports with steady local production.
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External-carbon systemsSafety validation

Growing plants without light

Feeding plants with no photons.

Testing plant systems that grow on external carbon—potentially from renewable-powered electrochemistry—so biomass formation does not depend on light. Researching energy efficiency, composition, and safety to identify niches where "no-light" cultivation makes sense.
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Closed hydroponicsSupply-chain audit

Hydroponic cotton in Europe

Premium cotton grown hydroponically for traceable, ethical luxury textiles.

Growing traceable, high-quality cotton in closed hydroponic systems and delivering a fully audited supply chain to French and Italian luxury mills—reducing water use and guaranteeing ethical labour while producing fibres suited to premium fabrics.
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Commercial pilot scaleHarvest systems

Vertical-farm cereals and high-protein aquatic crops

Growing staple cereals and ultra-high-protein aquatic crops indoors for resilient, local supply.

Adapting vertical farming to produce wheat, rye and ultra-high-protein Wolffia at commercial pilot scale—optimising growth cycles, harvest systems, and processing pathways so urban food systems can access local grain and novel proteins with predictable quality.
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