Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Remote Control of Greenhouse Vegetable Production with Artificial Intelligence-Greenhouse Climate, Irrigation, and Crop Production
Hemming, Silke ; Zwart, Feije de; Elings, Anne ; Righini, Isabella ; Petropoulou, Anna - \ 2019
Sensors 19 (2019)8. - ISSN 1424-8220
artificial intelligence - crop production - indoor farming - resource use efficiency - sensors

The global population is increasing rapidly, together with the demand for healthy fresh food. The greenhouse industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI) has reached breakthroughs in several areas, however, not yet in horticulture. An international competition on "autonomous greenhouses" aimed to combine horticultural expertise with AI to make breakthroughs in fresh food production with fewer resources. Five international teams, consisting of scientists, professionals, and students with different backgrounds in horticulture and AI, participated in a greenhouse growing experiment. Each team had a 96 m2 modern greenhouse compartment to grow a cucumber crop remotely during a 4-month-period. Each compartment was equipped with standard actuators (heating, ventilation, screening, lighting, fogging, CO2 supply, water and nutrient supply). Control setpoints were remotely determined by teams using their own AI algorithms. Actuators were operated by a process computer. Different sensors continuously collected measurements. Setpoints and measurements were exchanged via a digital interface. Achievements in AI-controlled compartments were compared with a manually operated reference. Detailed results on cucumber yield, resource use, and net profit obtained by teams are explained in this paper. We can conclude that in general AI performed well in controlling a greenhouse. One team outperformed the manually-grown reference.

Autonomous Greenhouse Challenge, First Edition (2018)
Hemming, S. ; Zwart, H.F. de; Elings, A. ; Righini, I. ; Petropoulou, Anna - \ 2019
climate set points - crop management - cucumber - greenhouse climate - harvest - irrigation - outside weather - pruning - resource consumption
The dataset contains data on greenhouse climate, irrigation, outside weather, greenhouse climate set points, harvest and crop management, resource consumption. Data were collected during a 4-month cucumber production (cv. Hi Power) in 6 glasshouse compartments (96 m2), located in Bleiswijk (The Netherlands). The dataset contains raw and processed data. Raw data were collected via climate measuring boxes, climate and irrigation process computer, manual registrations, outside weather station. The dataset was produced during the first edition of Autonomous Greenhouse Challenge, an international competition aiming at using Artificial Intelligence algorithms for the remote control of greenhouse horticulture production. Five international teams consisting of scientists, professionals and students participated in this experiment. The teams' names are: iGrow, deep_greens, AiCU, Sonoma, Croperators. They developed AI algorithms to remotely determine the Climate control set points and they additionally sent instructions for the crop pruning strategy. They had to realize the highest yield with minimal use of resources (e.g. water, CO2). The achievements in AI-controlled compartments were compared with a reference compartment, operated manually by three Dutch commercial growers (named Reference).
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