Support system for decision making in the management of the greenhouse environmental based on growth model for sweet pepper

Sánchez-Molina, J.A.; Pérez, Neyo; Rodríguez, Francisco; Guzmán, José Luis; López, J.C.

doi:10.1016/j.agsy.2015.06.009

Cited by 14 publications

(8 citation statements)

References 29 publications

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“…The management of production in a greenhouse requires decision making on several tasks and time scales. These decisions are mainly related to management of the crop growth conditions [195] , the culture period and practice such as seedling production [196] , event-based irrigation [197] and the control and management of environment based growth models [198] . Research and development in decision support system (DSS) for greenhouse application began during the 1990s, primarily for recommending microclimate reference values and set-points, and for pest and disease management.…”

Section: Decision Support Systemsmentioning

confidence: 99%

Advances in greenhouse automation and controlled environment agriculture: A transition to plant factories and urban agriculture

Shamshiri

Kalantari

Ting

et al. 2018

International Journal of Agricultural and Biological Engineering

274

185

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Greenhouse cultivation has evolved from simple covered rows of open-fields crops to highly sophisticated controlled environment agriculture (CEA) facilities that projected the image of plant factories for urban agriculture. The advances and improvements in CEA have promoted the scientific solutions for the efficient production of plants in populated cities and multi-story buildings. Successful deployment of CEA for urban agriculture requires many components and subsystems, as well as the understanding of the external influencing factors that should be systematically considered and integrated. This review is an attempt to highlight some of the most recent advances in greenhouse technology and CEA in order to raise the awareness for technology transfer and adaptation, which is necessary for a successful transition to urban agriculture. This study reviewed several aspects of a high-tech CEA system including improvements in the frame and covering materials, environment perception and data sharing, and advanced microclimate control and energy optimization models. This research highlighted urban agriculture and its derivatives, including vertical farming, rooftop greenhouses and plant factories which are the extensions of CEA and have emerged as a response to the growing population, environmental degradation, and urbanization that are threatening food security. Finally, several opportunities and challenges have been identified in implementing the integrated CEA and vertical farming for urban agriculture.

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Section: Decision Support Systemsmentioning

confidence: 99%

Advances in greenhouse automation and controlled environment agriculture: A transition to plant factories and urban agriculture

Shamshiri

Kalantari

Ting

et al. 2018

International Journal of Agricultural and Biological Engineering

274

185

View full text Add to dashboard Cite

show abstract

“…There are some common models utilized for this purpose, such as LP PYRA 02 [4], E+E Elektronik EE160 [18], DHT11 [19,20], and LM35 model [21]. On the other hand, Humidity sensors can be utilized for sensing the amount of vapours in the air, such as LP PYRA 02 [4], E+E Elektronik EE160 [18], DHT11 [19,20], and HSM20G [21]. Additionally, there is another important sensing technology need to be utilized inside the greenhouse for measuring the level of CO 2 .…”

Section: A Iot Sensorsmentioning

confidence: 99%

“…Additionally, there is another important sensing technology need to be utilized inside the greenhouse for measuring the level of CO 2 . It is available in markets with different models such as Vaisala GMP220 [4]. Sensors for sensing the density of Sun rays is also another kind of critical and sensitive technology required in the greenhouse visible, such as LP PYRA 02 [4], and Apogee Instruments Inc. SP110 [18].…”

Section: A Iot Sensorsmentioning

confidence: 99%

“…This makes greenhouses is an appropriate economical solution for farming because climate variables can be manipulated and controlled to achieve optimal growth rate of crops. Greenhouse allows producing crops, especially fruits and vegetables production that requires cold weather to grow fast in a quality manner, all year round and meet consumer demand for out-of-season fruit and vegetables [4]. The Internet of Things (IoT) concept allows the system, using electronic circuits, sensors and programming, to detect and control other devices remotely, creating a good interaction between the physical and computer world in order to improve efficiency and accuracy while achieving financial benefits [5].…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent IoT-Based System Design for Controlling and Monitoring Greenhouse Temperature

2020

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The Kingdom of Saudi Arabia is known for its extreme climate where temperatures can exceed 50 °C, especially in summer. Improving agricultural production can only be achieved using innovative environmentally suitable solutions and modern agricultural technologies. Using Internet of Things (IoT) technologies in greenhouse farming allows reduction of the immediate impact of external climatic conditions. In this paper, a highly scalable intelligent system controlling, and monitoring greenhouse temperature using IoT technologies is introduced. The first objective of this system is to monitor the greenhouse environment and control the internal temperature to reduce consumed energy while maintaining good conditions that improve productivity. A Petri Nets (PN) model is used to achieve both monitoring of the greenhouse environment and generating the suitable reference temperature which is sent later to a temperature regulation block. The second objective is to provide an Energy-Efficient (EE) scalable system design that handles massive amounts of IoT big data captured from sensors using a dynamic graph data model to be used for future analysis and prediction of production, crop growth rate, energy consumption and other related issues. The design tries to organize various possible unstructured formats of raw data, collected from different kinds of IoT devices, unified and technology-independent fashion using the benefit of model transformations and model-driven architecture to transform data in structured form.

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“…Modeling of crop growth can be used to better understand the physiological response to changes in environmental conditions and allow strategies to be applied [7]. The environment control in a greenhouse must be determined based on the desired crop performance; therefore, a crop growth model is important to manipulate the environment conditions to achieve optimal plant growth and development [8,9].…”

Section: Introductionmentioning

confidence: 99%

Development of Growth Model for Grafted Hot Pepper Seedlings as Affected by Air Temperature and Light Intensity

Kwack

Kim

2021

Sustainability

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The objective of this study was to develop a growth model for grafted hot pepper seedlings as affected by air temperature and light intensity. After grafted union formation, the hot pepper seedlings were cultivated in various environmental factors in terms of four levels, mean daily air temperature (17, 22, 27, and 32 °C) and 3 levels of light intensity (150, 350, and 550 μmol·m−2·s−1). The growth traits were measured 0, 7, 14, 21, and 28 days after grafted union formation (DAGU). The plant height was improved, and development of leaves enhanced by higher air temperature. The number of leaves was greatest under the combination of the high temperature and high light intensity, resulting in 39.0/plant at 28 DAGU. The leaf area and dry weight showed 491.9 cm2/plant and 2.68 g/plant, respectively, at 28 DAGU under 32 °C air temperature and 550 μmol·m−2·s−1 light intensity. The changes of dry weight were rapidly increased under the higher air temperature and light intensity as followed by analysis of the growth curve. The beta distribution model was developed, and the relative growth rate (RGR) was simulated by the model, the maximum RGR was predicted at 0.116 g·g·d−1. The RGR showed 0.113, 0.127, and 0.109 g·g·d−1 at 10, 20, and 30 °C air temperature, respectively, and RGR was improved by 12% by increasing the air temperature by 10 °C, without going over 25 °C ADT. Results indicated that the developed growth model might be applied to optimal environmental control for maximized RGR of production of grafted hot pepper seedlings.

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Support system for decision making in the management of the greenhouse environmental based on growth model for sweet pepper

Cited by 14 publications

References 29 publications

Advances in greenhouse automation and controlled environment agriculture: A transition to plant factories and urban agriculture

Advances in greenhouse automation and controlled environment agriculture: A transition to plant factories and urban agriculture

An Intelligent IoT-Based System Design for Controlling and Monitoring Greenhouse Temperature

Development of Growth Model for Grafted Hot Pepper Seedlings as Affected by Air Temperature and Light Intensity

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