Remote control of greenhouse cucumber production with artificial intelligence - results from the first international autonomous greenhouse challenge
| Autor: | S. Hemming, I. Righini, F. de Zwart, A. Elings |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Acta Horticulturae, 1296, 987-996 Acta Horticulturae 1296 (2020) |
| ISSN: | 0567-7572 |
| Popis: | The world population is increasing rapidly together with the demand for healthy fresh food. Greenhouse industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI) reaches breakthroughs in several areas, however, not yet in horticulture. An international competition on “autonomous greenhouses” aims in combining horticultural expertise with AI to make breakthroughs in fresh food production with fewer resources. Five international teams with different background in horticulture and AI participated in a growing experiment. Each team had a 96-m2 modern greenhouse compartment at WUR to grow remotely a cucumber crop ('Hi-Power') during a 4-months period. Each compartment was equipped with standard actuators and sensors, heating, ventilation, screening, artificial lighting, fogging, CO2 supply and irrigation. Climate control set points were remotely determined by teams using own AI algorithms, actuators were operated by a climate and irrigation process computer. Additionally, teams sent instructions for the crop pruning strategy. Measurements and set points were exchanged via a digital interface. Achievements in AI-controlled compartments were compared with a reference compartment, operated manually by three commercial growers. Teams used additional individually chosen sensors such as RGB or thermal cameras, temperature-humidity-light-sensor-networks, root-zone sensors, sap-flow meters or crop-weighing sensors. Teams' strategies for remote control ranged from supervised, unsupervised and reinforcement machine learning. Teams were judged and received points on several aspects: 50% for net profit, 20% for sustainability indicators, energy- and water-use-efficiency and CO2 consumption, 30% for their artificial intelligence algorithms, novelty, level of autonomous control, robustness and scalability. The results obtained by different teams in terms of climate and growing strategies, different resource use efficiency and net profit are presented in this paper. One of the AI-controlled compartments achieved overall better results than the manual grown reference compartment. |
| Databáze: | OpenAIRE |
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