{
  "id": "AIBD-0200",
  "title": "AI teams out-earn human growers in Wageningen's Autonomous Greenhouse Challenges (cucumber 2018, cherry tomato 2020)",
  "summary": "2018 cucumber challenge: winning algorithm raised sellable yield 6% and net profit 17% over reference growers; 2020 cherry-tomato challenge: all five AI compartments beat the growers' net profit.",
  "date_observed": "2020-05-29",
  "date_basis": "unassessed",
  "date_precision": "unassessed",
  "benefit_status": null,
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  "account": "Wageningen University & Research runs the Autonomous Greenhouse Challenge in six identical 96 m² high-tech compartments at Bleiswijk, with the organizing team publishing the results. In the first challenge in 2018, five international teams grew cucumber remotely for four months. They set climate, irrigation and crop setpoints with their own AI algorithms. One team outperformed the manually operated reference. The 2020 paper reports that the winning algorithm increased commercially sellable production by 6% and net profit by 17% over the reference growers. In the second challenge, cherry tomato cv. Axiany was transplanted on 16 December 2019. Five teams (Automatoes, AiCU, DIGILOG, IUA.CAAS, The Automators) took remote control on 20 December 2019. Harvest ran from 13 February to 29 May 2020. Net profit ranged from 3.10 to 6.86 €/m² and all AI-operated compartments exceeded the Dutch growers' reference compartment. Compartment 306 (Automatoes) had the highest income (37.2 €/m²) and highest net profit, while the reference had high energy and labor costs. The challenge was funded by Tencent. A third edition grew lettuce in 2023. Net profit is computed from the organizers' price and cost model in an experimental facility, not a commercial farm.",
  "category": "general benefit",
  "unexpected": false,
  "purpose": "Control greenhouse climate, irrigation and crop management remotely with AI algorithms to maximize net profit with fewer resources.",
  "system": "Team AI algorithms in the Autonomous Greenhouse Challenge (2020: Automatoes, AiCU, DIGILOG, IUA.CAAS, The Automators)",
  "model_version": null,
  "developer": "International competing teams; challenge organized by Wageningen University & Research and funded by Tencent",
  "deployer": "Wageningen University & Research greenhouse compartments, Bleiswijk",
  "location": "Bleiswijk, Netherlands",
  "domain": "Horticulture / greenhouse production",
  "segment": "Greenhouse climate and irrigation control",
  "benefit_type": "Increased Product Revenue",
  "taxonomy_note": "The profit gains come from an experimental greenhouse with simulated economics.",
  "registry_ids": {},
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    {
      "group": "organizational",
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      "reason": "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."
    },
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      "group": "societal",
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      "reason": "Resource-use differences are reported per compartment; no wider outcome is documented."
    }
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    "status": "assessed",
    "statement": "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."
  },
  "evidence": [
    {
      "kind": "peer-reviewed study",
      "citation": "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",
      "title": "Cherry Tomato Production in Intelligent Greenhouses - Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality",
      "url": "https://doi.org/10.3390/s20226430",
      "date": "2020-11-11",
      "written_by": "deployer",
      "quote": "Net profit ranged from 3.10 to 6.86 €/m². All AI operated greenhouses were able to reach higher net profits than the reference."
    },
    {
      "kind": "peer-reviewed study",
      "citation": "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",
      "title": "Remote Control of Greenhouse Vegetable Production with Artificial Intelligence - Greenhouse Climate, Irrigation, and Crop Production",
      "url": "https://doi.org/10.3390/s19081807",
      "date": "2019-04-16",
      "written_by": "deployer",
      "quote": "We can conclude that in general AI performed well in controlling a greenhouse. One team outperformed the manually-grown reference."
    }
  ],
  "replication": {
    "produced_by": [
      "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"
    ],
    "must_hold": [
      "The organizers' simulated price and cost model for net profit",
      "Sensor data exchanged with teams throughout the season"
    ],
    "echoes": [
      {
        "date": "2023-03-08",
        "setting": "Third challenge: fully autonomous lettuce in six compartments over two cultivation cycles",
        "model_version": "Team algorithms with computer vision (Deeplabv3+ in detectron2 v0.6)",
        "outcome": "unknown",
        "source": "Petropoulou AS et al. 2023, Sensors 23(6):2929, doi:10.3390/s23062929"
      }
    ]
  },
  "paired_incident": null,
  "headline": "AI teams out-earn growers in Wageningen's greenhouse challenges",
  "metric": "winning algorithm raised sellable yield 6% and net profit 17% over reference growers",
  "comparator": null,
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  "value": null,
  "developer_country": null,
  "deployer_country": "Netherlands",
  "unrated_because": null,
  "brief_notes": null,
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    {
      "date": "2026-10-20",
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      "source": null,
      "editor": "AIBD"
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    {
      "date": "2026-10-21",
      "change": "Benefit type set to Increased Product Revenue under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.",
      "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/",
      "editor": "AIBD"
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    {
      "date": "2026-10-21",
      "change": "Country of the deployer added from the organization's own site or a registry.",
      "source": "https://en.wikipedia.org/wiki/Wageningen_University_%26_Research",
      "editor": "AIBD"
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    {
      "date": "2026-10-21",
      "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
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      "editor": "AIBD"
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  "confidence": "claimed",
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            "account": "Wageningen University & Research runs the Autonomous Greenhouse Challenge in six identical 96 m² high-tech compartments at Bleiswijk, with the organizing team publishing the results. In the first challenge in 2018, five international teams grew cucumber remotely for four months. They set climate, irrigation and crop setpoints with their own AI algorithms. One team outperformed the manually operated reference. The 2020 paper reports that the winning algorithm increased commercially sellable production by 6% and net profit by 17% over the reference growers. In the second challenge, cherry tomato cv. Axiany was transplanted on 16 December 2019. Five teams (Automatoes, AiCU, DIGILOG, IUA.CAAS, The Automators) took remote control on 20 December 2019. Harvest ran from 13 February to 29 May 2020. Net profit ranged from 3.10 to 6.86 €/m² and all AI-operated compartments exceeded the Dutch growers' reference compartment. Compartment 306 (Automatoes) had the highest income (37.2 €/m²) and highest net profit, while the reference had high energy and labor costs. The challenge was funded by Tencent. A third edition grew lettuce in 2023. Net profit is computed from the organizers' price and cost model in an experimental facility, not a commercial farm.",
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}