Intelligent Control for Plant Production System

Morimoto, T.; Hatou, Kenji; Hashimoto, Yasushi

doi:10.1016/s1474-6670(17)45554-6

Cited by 31 publications

(10 citation statements)

References 23 publications

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“…In this chapter we provide a basic preview of current trends and selected related work to hydroponics and aeroponics done so far. For example, the optimization of long-term plant growth in hydroponics, a hierarchical intelligent control system consisting of an expert system and a hybrid system based on genetic algorithms and neural networks was proposed in [10]. These two control systems were used appropriately, depending on the plant growth.…”

Section: Current Trends and Related Workmentioning

confidence: 99%

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Aeroponic Greenhouse as an Autonomous System Using Intelligent Space for Agriculture Robotics

Pala

Mizenko

Mach

et al. 2014

Advances in Intelligent Systems and Computing

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Abstract. This paper describes a novel approach to aeroponic and hydroponic system monitoring, fault detection and automation. The first part of this paper is dedicated to brief literature preview about hydroponics and aeroponics, its common and distinctive features and the description of the needs for its automation. The second part of this paper deals with aeroponic greenhouse control scheme proposal. We consider a greenhouse covered by a sensor network, actuators and hydroponic or aeroponic platforms to be a robotic system in so called intelligent space. The aeroponic platform design is described besides the conclusions and future work ideas in the last part of this paper.

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Section: Current Trends and Related Workmentioning

confidence: 99%

“…The set-points from the expert system were similar to those used by a skilled grower. Further details and experimental results can be found in [10].…”

Section: Current Trends and Related Workmentioning

confidence: 99%

Aeroponic Greenhouse as an Autonomous System Using Intelligent Space for Agriculture Robotics

Pala

Mizenko

Mach

et al. 2014

Advances in Intelligent Systems and Computing

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“…The advantages of using evolutionary methods over other techniques of system optimisation are relatively obvious and easy to implement when the fitness of the system is easy to determine, such as the growth rate of a plant (Morimoto et al 1996) or comparing the predictions of a model against reality (Noever et al 1996). However, when the fitness is calculated subjectively it is easy to end up with a system that does not actually give meaningful or useful results.…”

Section: Evolutionmentioning

confidence: 99%

The state of play in machine/environment interactions

Aitkenhead

McDonald

2006

Artif Intell Rev

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Due to the breadth of the subject, it is no longer possible to provide a review of all of the work being carried out in the field of Artificial Intelligence. However, a more localised review of research taking place in the overlap between engineering, AI and psychology can be meaningfully performed. We show here that while there have been marked successes in the past few years, there is an identifiable set of 'classic' problems that remain to be solved, and which largely direct the work ongoing in this area. This review aims to discuss the directions being taken at the current time, in particular the developing and maturing possibilities provided by neural networks and evolutionary computation, and by the use of our knowledge of the mind in developing artificial agents capable of mimicking our abilities to interact with the environment.

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“…Another method for constructing a dynamic model is system identification that deals with unknown processes. The identification methods have been applied to plant production systems (Morimoto et al, 1996;2003;Morimoto and Hashimoto, 2000). It is possible to adapt the time-variation of the physiological status of a plant if the identification procedure is repeated periodically as the plant grows.…”

Section: Introductionmentioning

confidence: 99%

“…They are powerful tools for identifying complex systems characterized by non-linearity because they can acquire such nonlinear characteristics using their own high learning capabilities (Rumelhart et al, 1986). In a plant production system, therefore, artificial neural networks have been widely applied to identify the physiological responses of plant (Morimoto et al, 1996;2003;Qiao et al, 2010;Hatzig et al, 2015;Ghamarnia and Jalili, 2015).…”

Section: Introductionmentioning

confidence: 99%

Identifying and Modelling the Dynamic Response of Leaf Water Content to Water Temperature in Hydroponic Tomato Plant

Yumeina

Morimoto

2017

Environmental Control in Biology

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In this paper, we identified and modelled the short-term response of leaf water content to water temperature in hydroponic tomato plants using a neural network. Leaf water content was estimated from leaf thickness using an eddy current-type displacement sensor. Dynamic changes in the leaf water content of the tomato plants, as affected by water temperatures, was identified and modelled using a neural network. A three-layered neural network with optimal system order and hidden neuron number allowed nonlinearity of this system to be successfully identified. The estimated responses obtained from model simulation were correlated closely with the observed responses. Leaf water content increased with water temperature up to 35°C in a short period of several hours. At a water temperature above 35°C, however, leaf water content decreased with increasing water temperature. Leaf water content, including root water uptake, of tomato plants is significantly suppressed by high water temperature. The relationship between water temperature and leaf water content over a short-term of several hours is represented by a hill-shaped curve (nonlinear curve) and reaches maximum value when the water temperature is about 35°C. These dynamic and static relationships between water temperature and leaf water content were successfully confirmed from model simulation.

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Intelligent Control for Plant Production System

Cited by 31 publications

References 23 publications

Aeroponic Greenhouse as an Autonomous System Using Intelligent Space for Agriculture Robotics

Aeroponic Greenhouse as an Autonomous System Using Intelligent Space for Agriculture Robotics

The state of play in machine/environment interactions

Identifying and Modelling the Dynamic Response of Leaf Water Content to Water Temperature in Hydroponic Tomato Plant

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