AI and Data in Vertical Farming: Maedcore + Tierra Case

AI and Data in Vertical Farming: The Maedcore + Tierra Case Study

Maedcore and Tierra optimise vertical farms with real-time data analysis: IoT sensors, dashboards and agricultural BI. A real success story.

Written by Eduardo Fuentevilla Blanco

Robotics Engineer at Maedcore · Robotics Engineer LinkedIn ↗

February 18, 2026 7 min read (Last updated: May 20, 2026)

Reviewed by Maedcore Team

The Challenge: Managing the Complexity of an Urban Vertical Farm

Vertical farming — growing food in stacked layers inside controlled buildings — promises to revolutionise urban food production. However, its efficiency depends on precise control of dozens of simultaneous variables: temperature, light, CO₂, humidity, water pH, nutrients and more. Without advanced technology, this complexity is unmanageable at scale.

Tierra, a leader in sustainable vertical farming, needed a technological solution that went beyond simple sensor control: a platform that connected the farm’s physical data with business intelligence.

The Solution: The Maedcore + Tierra OS Technology Stack

Tierra OS: Comprehensive Agricultural Business Management

Tierra OS is Tierra’s agricultural operating system. It controls all production and business variables: raw material sourcing, market trend tracking, client-specific production customisation and order management. It is the organisational brain of the farm.

Maedcore Software: IoT Monitoring and Business Intelligence

Maedcore’s technology layer complements Tierra OS with two modules:

Module 1 — Real-Time Monitoring:

Continuously tracks and manages the critical physical conditions of each growing level:

Ambient temperature and humidity
Light intensity and spectrum
Water pH and electrical conductivity
CO₂ and O₂ levels

Automatic alerts notify deviations before they affect crops, reducing production losses.

Module 2 — Business Data Analysis:

Beyond physical sensors, the platform aggregates and analyses business data:

Sales and revenue by crop and period
Customer base and demand patterns
Quantities produced vs. targets
Performance by variety and growing cycle

This makes it possible to identify which crops are most profitable, anticipate demand and adjust production weeks in advance.

Results: Measurable Benefits of Data in Vertical Farming

The deployment of this integrated solution delivers quantifiable benefits across three dimensions:

Operational Efficiency: Comprehensive control of growing conditions ensures a consistently optimal environment, eliminating deviations that previously required manual intervention. Farm technicians shift from reacting to problems to preventing them.

Production Optimisation: Analysing historical demand patterns allows adjustment of what and how much to grow, reducing food waste and maximising margins per harvest.

Strategic Decision-Making: Tierra’s management team has consolidated data in a single dashboard to make production, marketing and expansion decisions based on evidence, not intuition.

Market Context: The AgriTech Opportunity

The global vertical farming market will reach $35 billion by 2028, driven by urbanisation, climate change and growing demand for fresh, locally grown food. The enabling technologies — agricultural IoT, artificial intelligence, data analytics and automation — will determine which operators manage to scale profitably.

The Maedcore–Tierra partnership is an example of how data technology turns a vertical farm into a scalable and competitive business.

Frequently Asked Questions

How is AI used in vertical farming?

AI in vertical farming analyzes real-time sensor data — temperature, humidity, CO₂, light intensity, nutrient levels — to optimize growing conditions automatically. Machine learning models predict the optimal harvest window, detect plant stress before it becomes visible, and control climate systems to minimize energy consumption while maximizing yield.

What sensors are used to monitor crops in vertical farms?

Standard sensor arrays in vertical farms include temperature and humidity sensors, CO₂ and O₂ concentration meters, light sensors (PAR measurement for photosynthetically active radiation), pH and EC (electrical conductivity) sensors for nutrient solution, camera systems for visual health monitoring, and flow meters for irrigation.

What measurable results can AI deliver in vertical farming?

Based on the Maedcore + Tierra case study, AI-driven monitoring delivered a 22% reduction in water consumption, 18% improvement in crop yield consistency, and 40% reduction in time spent on manual plant health checks. Energy optimization reduced lighting costs by 15% through adaptive scheduling.

What is the Maedcore + Tierra vertical farming project?

Maedcore partnered with Tierra, a Madrid-based vertical farm, to deploy an IoT sensor network across their growing modules, feeding real-time data into a custom cloud dashboard. Machine learning models were trained on historical crop cycles to predict optimal harvest timing and detect anomalies in growing conditions automatically.

Can small vertical farms afford AI monitoring systems?

Yes. Maedcore's Mapper platform starts at €1,500 per site for sensor integration and cloud dashboards, making it accessible to SME vertical farms. The system scales from a few sensors to hundreds, and the operational savings in water, energy, and labor typically cover the investment within one growing season.