Development and analysis of dynamical mathematical models of greenhouse climate: A review

López‐Cruz, Irineo L.; Fitz-Rodríguez, E.; Moreno, Raquel Salazar; Rojano-Aguilar, Abraham; Kaçıra, Murat

doi:10.17660/ejhs.2018/83.5.1

Cited by 38 publications

(23 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mathematical dynamic models are built on scientific knowledge that describes the complex interactions within a greenhouse system. They enable a quantitative approach of the greenhouse system as transparent mechanistic models [13], allowing for optimization algorithms to find an optimal control, and they are physically interpretable.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production

Hemming

Zwart

Elings

et al. 2019

Sensors

108

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

“…In order to add more automated control, various greenhouse climate and crop models have been developed. An overview of today’s greenhouse climate models is given in a previous study [13]. An overview of greenhouse crop models and modelling approaches are given in other studies [14,15].…”

Section: Introductionmentioning

confidence: 99%

Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production

Hemming

Zwart

Elings

et al. 2019

Sensors

108

View full text Add to dashboard Cite

show abstract

“…Dynamic climate models have been developed [ 10 , 18 , 23 , 24 , 25 , 26 , 27 , 28 ] which act as a digital twin of the real greenhouse. An overview of today’s greenhouse climate models is given in a previous study [ 29 ]. Mechanistic models give the opportunity to be used for intelligent decision support on climate control actions.…”

Section: Introductionmentioning

confidence: 99%

Cherry Tomato Production in Intelligent Greenhouses—Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality

Hemming

Zwart

Elings

et al. 2020

Sensors

View full text Add to dashboard Cite

Greenhouses and indoor farming systems play an important role in providing fresh and nutritious food for the growing global population. Farms are becoming larger and greenhouse growers need to make complex decisions to maximize production and minimize resource use while meeting market requirements. However, highly skilled labor is increasingly lacking in the greenhouse sector. Moreover, extreme events such as the COVID-19 pandemic, can make farms temporarily less accessible. This highlights the need for more autonomous and remote-control strategies for greenhouse production. This paper describes and analyzes the results of the second “Autonomous Greenhouse Challenge”. In this challenge, an experiment was conducted in six high-tech greenhouse compartments during a period of six months of cherry tomato growing. The primary goal of the greenhouse operation was to maximize net profit, by controlling the greenhouse climate and crop with AI techniques. Five international teams with backgrounds in AI and horticulture were challenged in a competition to operate their own compartment remotely. They developed intelligent algorithms and use sensor data to determine climate setpoints and crop management strategy. All AI supported teams outperformed a human-operated greenhouse that served as reference. From the results obtained by the teams and from the analysis of the different climate-crop strategies, it was possible to detect challenges and opportunities for the future implementation of remote-control systems in greenhouse production.

show abstract

“…The dynamic models of greenhouse climate are classified in mechanistic and black-box models (ARX) [7]. Mechanistic models describe the system it is simulating based on knowledge of the processes that are taking place [8].…”

Section: Climate Modeling In Greenhousementioning

confidence: 99%

Greenhouse Crop Simulation Models and Microclimate Control Systems, A Review

Rezvani¹,

Shamshiri²,

Hameed³

et al. 2021

Next-Generation Greenhouses for Food Security

View full text Add to dashboard Cite

A greenhouse is a complex environment in which various biological and non-biological phenomena occur. For simulation and prediction of the climate and plant growth changes in the greenhouse are necessary to provide mathematical models. The dynamic greenhouse climate models are classified in mechanistic and black-box models (ARX). Climatic models are mainly obtained using energy balance or computational fluid dynamics. In the energy balance models, the greenhouse climatic variables are considered uniformity and homogeneity, but in the computational fluid dynamics, the heterogeneity of the greenhouse environment can be shown by 3D simulation. Crop growth simulation models are quantitative tools based on scientific principles and mathematical relationships that can evaluate the different effects of climate, soil, water, and crop management factors on crop growth and development. In this chapter, with a review of the basics of climate models in greenhouses, the results and application of some climate dynamics models based on the energy balance as well as simulations performed with computational fluid dynamics are reviewed. A review of greenhouse growth models and functional–structural plant models (FSPM) was also conducted.

show abstract

Development and analysis of dynamical mathematical models of greenhouse climate: A review

Cited by 38 publications

References 43 publications

Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production

Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production

Cherry Tomato Production in Intelligent Greenhouses—Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality

Greenhouse Crop Simulation Models and Microclimate Control Systems, A Review

Contact Info

Product

Resources

About