Modelling and control of greenhouse system using neural networks

Manonmani, A.; Thyagarajan, T.; Elango, M.; Subbian, Sutha

doi:10.1177/0142331216670235

Cited by 44 publications

(27 citation statements)

References 10 publications

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“…The models could not impute T in , although this factor also has somewhat constant patterns because the internal environments of greenhouses are controlled to be within specific ranges [ 4 ]. Neural network algorithms yielded high performance in previous studies [ 35 , 36 ]. Unlike T out , T in could be affected by different grower strategies [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

Accurate Imputation of Greenhouse Environment Data for Data Integrity Utilizing Two-Dimensional Convolutional Neural Networks

Moon

Lee

Son

2021

Sensors

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Greenhouses require accurate and reliable data to interpret the microclimate and maximize resource use efficiency. However, greenhouse conditions are harsh for electrical sensors collecting environmental data. Convolutional neural networks (ConvNets) enable complex interpretation by multiplying the input data. The objective of this study was to impute missing tabular data collected from several greenhouses using a ConvNet architecture called U-Net. Various data-loss conditions with errors in individual sensors and in all sensors were assumed. The U-Net with a screen size of 50 exhibited the highest coefficient of determination values and the lowest root-mean-square errors for all environmental factors used in this study. U-Net50 correctly learned the changing patterns of the greenhouse environment from the training dataset. Therefore, the U-Net architecture can be used for the imputation of tabular data in greenhouses if the model is correctly trained. Growers can secure data integrity with imputed data, which could increase crop productivity and quality in greenhouses.

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Section: Discussionmentioning

confidence: 99%

Accurate Imputation of Greenhouse Environment Data for Data Integrity Utilizing Two-Dimensional Convolutional Neural Networks

Moon

Lee

Son

2021

Sensors

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“…Compared with the method of searching optimal gain parameters by GA, the adaptive control strategy has better adaptability, robustness and good control performance for a complex nonlinear greenhouse climate. Manonmani et al [164] proposed a neural network model based on the time series of a nonlinear autoregressive with external input (NARX) model. Based on this model, a control scheme of the nonlinear autoregressive moving average controller (NARMA-L2) was proposed.…”

Section: A Monitoring and Control Of Environmentmentioning

confidence: 99%

Equipment and Intelligent Control System in Aquaponics: A Review

Wei

et al. 2019

IEEE Access

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Traditional planting and aquaculture has the problem of large consumption of water resources and land resources, and the water environmental pollution is also a difficult problem facing human beings. Population growth and food safety issues have promoted the concept of aquaponics-a recycling ecological planting and breeding mode. It combines hydroponics and recirculating aquaculture technology to realize water resources and nutrient recycling, low pollution and high productivity and efficiency. In this paper, hydroponics as the main vegetable cultivation method in aquaponics and the main equipment of water treatment in recirculating aquaculture are introduced, and the traditional equipment and its development prospects are analyzed. The greenhouse environments, water quality and nutrient circulation involved in intelligent monitoring and control of aquaponic systems are systematically analyzed and summarized. This paper summarizes the current development of technology and methods in aquaponics and provides prospects for future development trends. With the development of technology, in the future, the aquaponics system will become more intelligent, intensive, accurate and efficient. INDEX TERMS Aquaponics, efficient utilization of water, intelligent controlling, nutrient cycle, precision monitoring.

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“…Generally, the artificial neural network has been utilized for system identification and controller in wide areas of applications from the industrial nonlinear process to the vehicle control system. These included the thermal dynamic of pulsating heat pipe [49] and greenhouse temperature [50] to the autonomous vehicle control [51] and UAVs [19,23,24].…”

Section: The Direct Inverse Control Of the Ann-based Controllermentioning

confidence: 99%

Neural Network Control System of UAV Altitude Dynamics and Its Comparison with the PID Control System

Muliadi

Kusumoputro

2018

Journal of Advanced Transportation

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This article proposes a comparative method to assess the performance of artificial neural network's direct inverse control (DIC-ANN) with the PID control system. The comparison served as an analysis tool to assess the advantages of DIC-ANN over conventional control method for a UAV attitude controller. The development of ANN method for UAV control purposes arises due to the limitations of the conventional control method, which is the mathematical based model, involving complex expression, and most of them are difficult to be solved directly into analytic solution. Although the linearization simplified the solving process for such mathematical based model, omitting the nonlinear and the coupling terms is unsuitable for the dynamics of the multirotor vehicle. Thus, the DIC-ANN perform learning mechanism to overcome the limitation of PID tuning. Therefore, the proposed comparative method is developed to obtain conclusive results of DIC-ANN advantages over the linear method in UAV attitude control. Better achievement in the altitude dynamics was attained by the DIC-ANN compared to PID control method.

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Modelling and control of greenhouse system using neural networks

Cited by 44 publications

References 10 publications

Accurate Imputation of Greenhouse Environment Data for Data Integrity Utilizing Two-Dimensional Convolutional Neural Networks

Accurate Imputation of Greenhouse Environment Data for Data Integrity Utilizing Two-Dimensional Convolutional Neural Networks

Equipment and Intelligent Control System in Aquaponics: A Review

Neural Network Control System of UAV Altitude Dynamics and Its Comparison with the PID Control System

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