Eternal.ag raises €8M to automate greenhouse harvesting with AI-powered robots

2026-03-19 · Source: Tech.eu - Tech.eu · Field: Agriculture & Food Systems — Precision Agriculture & Smart Farming, Agricultural Sustainability & Climate · Depth: Fundamental Awareness, quick

Summary

Cologne-based startup Eternal.ag has secured €8 million in funding from investors including Simon Capital and Oyster Bay Venture Capital to advance its AI-powered autonomous harvesting robots for greenhouses. The company aims to address severe labor shortages, which have seen a 30% decline in Europe since 2010, threatening year-round fresh produce supply. Eternal.ag's first commercial product, "Harvester," is an autonomous robot designed for tomato greenhouses, capable of operating up to 22 hours daily. The system is modular, allowing for future expansion into broader greenhouse operations, with a long-term vision for fully automated greenhouses by 2040. The new capital will accelerate product development, expand commercial deployments across Europe, and support the integration of additional crop types.

Key takeaway

For greenhouse operators facing escalating labor shortages and seeking to ensure consistent produce supply, Eternal.ag's autonomous harvesting robots offer a viable solution. Investing in such AI-powered systems can stabilize operations, reduce reliance on manual labor, and contribute to a more resilient food supply chain. Consider piloting autonomous harvesters like "Harvester" to evaluate their impact on your operational efficiency and long-term sustainability.

Key insights

AI-powered autonomous robots are critical for addressing greenhouse labor shortages and ensuring year-round food supply.

Principles

• Greenhouse automation ensures operational continuity.
• Simulation-first development accelerates robot iteration.
• Robots learn and improve from real-world data.

Method

Eternal.ag employs a simulation-first development approach to train and test robots in virtual greenhouses, reducing iteration cycles from months to days. Deployed robots feed data back for continuous learning and improvement.

In practice

• Deploy autonomous harvesters for labor-intensive crops.
• Utilize modular robotic systems for future expansion.
• Implement data feedback loops for continuous robot improvement.

Topics

• Agricultural Robotics
• Greenhouse Automation
• Autonomous Harvesting
• AI-powered Systems
• Simulation-first Development

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Editorial summary, takeaway, and curation by AIssential. Original article published by Tech.eu - Tech.eu.