Key Takeaways
- Team MuGrow achieved 44 kg/m²/year—30% higher production than the reference grower—using Gardin’s real-time plant feedback technology.
- Profitability increased by 35%, rising to 60% with optimized harvest timing, generating an estimated €200,000/ha per year in added value.
- The trial validated the potential of bioautonomous growing, demonstrating the importance of direct plant measurements in autonomous greenhouse control.
- Gardin’s Plant Indicators guided lighting, climate, CO₂, and growth strategies, helping achieve higher yield quality and faster crop cycles.
- While full autonomy is still developing, growers are already using Gardin’s sensors to refine crop strategies and improve performance.
Autonomous Greenhouse Trial Validates Gardin’s Biofeedback Approach
Gardin’s plant measurement technology played a central role in the results achieved by Team MuGrow—comprised of TU Delft, Gardin, and Rijk Zwaan—in the 4th Autonomous Greenhouse Challenge. The team delivered an annualized yield equivalent to 44 kg/m²/year, significantly outperforming the reference grower by 30% and exceeding industry averages for high-wire cherry tomatoes by 45%.
The challenge required complete autonomy from potting to harvesting. MuGrow’s control strategy mirrored how human growers make decisions: observing the crop, evaluating production forecasts, and adjusting climate strategies. Gardin’s real-time plant feedback provided the physiological insight needed to optimize these decisions.
Gardin Plant Feedback Drives Higher Yields and Profitability
Gardin’s chlorophyll fluorescence sensor—measuring photosynthetic efficiency—enabled MuGrow to adapt lighting, temperature, and CO₂ strategies based on plant response rather than environmental assumptions. The outcome included:
- 340 g per pot yield
- 7.3% dry matter content (highest among competing teams)
- 69-day crop cycle (fastest in the challenge)
At a farm-gate price of €2/kg, the performance translated to an additional €200,000 per hectare per year compared to the reference greenhouse. Profits were 35% higher, increasing to 60% with refined harvest timing.
Plant Indicators Support Core Control Decisions
Four Gardin Plant Indicators guided key aspects of the cultivation strategy:
- HEALTH: Detected early signs of stress, helping safeguard long-term plant performance.
- BALANCE: Tracked source–sink dynamics to maintain consistent growth.
- EFFICIENCY: Identified optimal lighting levels and the point at which light becomes inefficient or damaging.
- PRODUCTIVITY: Quantified photosynthetic output to define cost-effective growth rates and optimal DLI targets.
These indicators supported decisions related to lighting, shading, CO₂, temperature, VPD, and heating—areas where environmental-only models often fall short.
Autonomous Systems Paired With Vision Models Improve Crop Management
Competition rules required teams to integrate computer vision for physical phenotyping, growth stage detection, and autonomous harvest triggers. MuGrow developed classifiers for vegetative, flowering, and fruiting stages, pot spacing, and harvest timing.
While conservative safety constraints delayed harvesting, earlier harvests could have increased annual profit by up to 50% by accelerating crop turnover.
Gardin’s Technology Already in Use by Commercial Growers
Although fully autonomous control is still emerging, Gardin’s sensor technology is already deployed in greenhouses worldwide. Growers use plant feedback to fine-tune climate strategies, manage stress, optimize lighting and CO₂, and support higher yields and quality.
Reflecting on the results, Gardin Head of Science Julian Godding noted that chlorophyll fluorescence offers one of the most powerful real-time insights into plant performance. TU Delft’s Robert D. McAllister added that reliable plant measurement is fundamental to advanced control strategies.
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