AI teams out-earn growers in Wageningen's greenhouse challenges

AI system

System: Team AI algorithms in the Autonomous Greenhouse Challenge (2020: Automatoes, AiCU, DIGILOG, IUA.CAAS, The Automators)
Model: Not assessed
Release: Not assessed
Application developer: International competing teams; challenge organized by Wageningen University & Research and funded by Tencent
Role: Not assessed
Configuration: Not assessed
Inputs and outputs: Not assessed
Autonomy: Not assessed
Workflow: Six identical 96 m² compartments with heating, ventilation, screening, lighting, fogging, CO2, water and nutrient actuators. Teams combining horticultural and AI expertise setting setpoints via a digital interface. A reference compartment run manually by Dutch growers.
Human oversight: Not assessed
Relation to AI: Teams' algorithms set climate and crop setpoints remotely and their compartments out-earned a single manually run reference compartment in both editions. The organizers (WUR) authored the peer-reviewed reports and are independent of the competing teams. Results come from a sponsored competition, not a production deployment.
AI contribution: Teams' algorithms set climate and crop setpoints remotely and their compartments out-earned a single manually run reference compartment in both editions. The organizers (WUR) authored the peer-reviewed reports and are independent of the competing teams. Results come from a sponsored competition, not a production deployment.

Outcome identifiers: Not assessed
Benefit: winning algorithm raised sellable yield 6% and net profit 17% over reference growers
Category: Increased Product Revenue
Status: Not assessed
Beneficiary groups: Organizations. Society.
Beneficiaries: Not assessed
Reach: Not assessed
Distribution and access: Not assessed
Duration: Not assessed
Expected?: Yes
Costs and harms: Not assessed

Studies: Not assessed
Study design: Not assessed
Measurement: Not assessed
Setting and sample: Not assessed
Measures: winning algorithm raised sellable yield 6% and net profit 17% over reference growers
Comparison: Not assessed
Results: winning algorithm raised sellable yield 6% and net profit 17% over reference growers
Additional value from AI: Not assessed
Limitations: Not assessed
Denominators: Not assessed
Materials for replication: Not assessed

Participants: Developer: International competing teams; challenge organized by Wageningen University & Research and funded by Tencent. Operator: Wageningen University & Research greenhouse compartments, Bleiswijk.
Jurisdictions: Benefit: Bleiswijk, Netherlands. Operator: Netherlands.
Ownership and control: Not assessed
Dependencies: Not assessed
Cross-border connections: Not assessed
Geopolitical implications: Not assessed

Conditions for replication: The organizers' simulated price and cost model for net profit. Sensor data exchanged with teams throughout the season.
Replication attempts: 1 follow-up entries. Outcome unresolved: 1.
Later findings: Not assessed
Availability: Not assessed
Institutional decision: Not assessed
Grounds: Not assessed
Action and result: Not assessed
Success criterion: Not assessed

Assessed benefits: Organizations. Society.
Depth: Not assessed
Reasons: Organizations: Net-profit gains (17% in 2018; 3.10–6.86 €/m² versus a lower reference in 2020) are from 96 m² experimental compartments. They use the organizers' price and cost model, not a grower's accounts. Society: Resource-use differences are reported per compartment; no wider outcome is documented.
Observed or intended: Not assessed
Rubric: Not assessed
Assessor and date: Not assessed
Alternative assessments: Not assessed
Fill: The deployer's account

Kind: Not assessed
Related entries: Not assessed
Field: Horticulture / greenhouse production. Greenhouse climate and irrigation control.
Purpose: Control greenhouse climate, irrigation and crop management remotely with AI algorithms to maximize net profit with fewer resources.
Dates: Date basis unassessed: 2020 (precision unassessed). Updated: 21 October 2026.
Stage: Not assessed
Version: SHA-256: b86f09c18fa3…

Peer-reviewed study Deployer

Hemming S, de Zwart F, Elings A, Petropoulou A, Righini I. Cherry Tomato Production in Intelligent Greenhouses - Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality. Sensors 20(22):6430 (2020). doi:10.3390/s20226430
Peer-reviewed study Deployer

Hemming S, de Zwart F, Elings A, Righini I, Petropoulou A. Remote Control of Greenhouse Vegetable Production with Artificial Intelligence - Greenhouse Climate, Irrigation, and Crop Production. Sensors 19(8):1807 (2019). doi:10.3390/s19081807