Control Strategies of Greenhouse Climate for Vegetables Production

López‐Cruz, Irineo L.; Fitz-Rodríguez, E.; Torres-Monsivais, J. C.; Trejo-Zúñiga, E. César; Ruíz‐García, Agustín; Ramírez-Árias, Armando

doi:10.1007/978-3-319-03880-3_14

Cited by 10 publications

(4 citation statements)

References 95 publications

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“…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 ].…”

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

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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.

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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

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“…[68] According to the summary of the review literature, over the years, several control algorithms have been developed and investigated in greenhouses to ensure ideal growing environments while reducing energy usage. [69] Control algorithms have been used to regulate greenhouse systems, including neural networks, PID, nonlinear, artificial neural network, optimal control, particle swarm optimization model predictive control (PSO-MPC), fuzzy logic, model predicted control, robust control, and hybrid control algorithms. [70,71] A summary and comparative analysis of the application of control algorithms in greenhouse research over the last 20 years is presented in Table 1.…”

Section: Methodologies Of Control Strategies In Greenhousementioning

confidence: 99%

Energy efficiency optimization strategies for greenhouse‐based crop cultivation: A review

Kaur,

Sonawane,

Rosha

2023

Can J Chem Eng

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Worldwide, food scarcity is becoming a debatable concern among the scientific fraternity due to the increased populace, leading to decreased arable land. This has compelled us to explore various innovative and technological solutions, for example, large‐scale greenhouse farming, to meet the surging demand for field production. In this context, research efforts have been continually made by various scientists and researchers to explore more control strategies/algorithms for keeping the indoor climate comfortable and enhancing the greenhouse's energy effectiveness. Considering this, an initiative was made to summarize the documented research findings in the last decade focusing on energy‐efficient greenhouse‐based crop cultivation. The findings of some studies considering selective parametric conditions have been presented in graphs/tables for reader clarity and discussion. Initially, the studies on existing energy efficient strategies, parameters, monitoring systems, sensing networks, and control algorithms have been discussed. A state of the art review found that control strategies are essential in low‐energy greenhouses since they influence crop yield and cost. It was observed that advanced control algorithms and energy conservation in greenhouses received more attention due to wide spread application, high compatibility, low‐cost, and user‐friendly operations. In terms of future perspectives, it is anticipated that the development of machine learning, big data, and artificial intelligence, combining these technologies with traditional and advanced control strategies would lead to a revolution in the management of greenhouse energy.

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“…Much research seeks to find these physical relations within the climate and crop (Vanthoor 2011;Stanghellini 1987;Jones et al 1991). While these mathematical models approach reality more closely, they can be complex and often need many parameters to work, making their implementation impractical and difficult (Lopez-Cruz et al 2013).…”

Section: Background and Related Workmentioning

confidence: 99%

Embedding a Long Short-Term Memory Network in a Constraint Programming Framework for Tomato Greenhouse Optimisation

Van Bokkem,

Van den Hemel,

Dumančić

et al. 2023

AAAI

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Increasing global food demand, accompanied by the limited number of expert growers, brings the need for more sustainable and efficient horticulture. The controlled environment of greenhouses enable data collection and precise control. For optimally controlling the greenhouse climate, a grower not only looks at crop production, but rather aims at maximising the profit. However this is a complex, long term optimisation task. In this paper, Constraint Programming (CP) is applied to task of optimal greenhouse economic control, by leveraging a learned greenhouse climate model through a CP embedding. In collaboration with an industrial partner, we demonstrate how to model the greenhouse climate with an LSTM model, embed this LSTM into a CP optimisation framework, and optimise the expected profit of the grower. This data-to-decision pipeline is being integrated into a decision support system for multiple greenhouses in the Netherlands.

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Control Strategies of Greenhouse Climate for Vegetables Production

Cited by 10 publications

References 95 publications

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

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

Energy efficiency optimization strategies for greenhouse‐based crop cultivation: A review

Embedding a Long Short-Term Memory Network in a Constraint Programming Framework for Tomato Greenhouse Optimisation

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