On Modeling Greenhouse Air-Temperature: an Experimental Validation

Hamad, Imen Haj; Chouchaine, Amine; Bouzaouache, Hajer

doi:10.1109/ssd52085.2021.9429311

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Following it, is converted to 4D and sent to predict against the trained model. It returns an array whose values are scales of similarities between the captured image and each disease [14]. The index of maximum values is determined to know the disease predicted by the model by comparing the test image with each class.…”

Section: Plant Disease Detection Using Pi Cameramentioning

confidence: 99%

Smart Greenhouse and Warehouse Monitoring with Disease Detection using Machine Learning

Savitha,

Rai,

Singh

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Global climatic changes in the recent era have resulted in a significant global food shortage. Where there is a vast population and a diverse agricultural sector, food security is a major problem. Growing competition for land, water, and energy will undermine our capital food production, and the effect of climate change will continue to be a challenge. The integrated greenhouse and warehouse provide quality and sustainable food production. In this paper an integrated green house with warehouse monitoring hardware implementation, with novel feature of customized disease detection with convolution neural network (CNN) algorithm is presented. The real time monitoring of data of different sensors are displayed on the screen/ dashboard developed by the android application. This aids farmers in making wise choices on crop management and disease detection of greenhouse crops and effective monitoring.

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Section: Plant Disease Detection Using Pi Cameramentioning

confidence: 99%

Smart Greenhouse and Warehouse Monitoring with Disease Detection using Machine Learning

Savitha,

Rai,

Singh

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…The first is based on the physical laws involved in the process and the second on the analysis of the input-output data of the model. In [3][4][5][6], the dynamic temperature model is based on the energy balance. The physical model of a greenhouse by researching thermal radiation and ventilation was developed in [7].…”

Section: Imentioning

confidence: 99%

A Takagi-Sugeno Fuzzy Model for Greenhouse Climate

Hamad

Chouchaine

Bouzaouache

2021

Eng. Technol. Appl. Sci. Res.

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This paper investigates the identification and modeling of a greenhouse's climate using real climate data from a greenhouse installed in the LAPER laboratory in Tunisia. The objective of this paper is to propose a solution to the problem of nonlinear time-variant inputs and outputs of greenhouse internal climate. Combining fuzzy logic technique with Least Mean Squares (LMS), a robust greenhouse climate model for internal temperature prediction is proposed. The simulation results demonstrate the effectiveness of the identification approach and the power of the implemented Takagi-Sugeno Fuzzy model-based algorithm.

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Testing and Analysis on the Spatial and Temporal Distribution of Light Intensity and CO2 Concentration in Solar Greenhouse

et al. 2023

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Greenhouses, as important parts of facility agriculture, can reduce the restrictions on agricultural production imposed by the natural environment and make rational and efficient use of production resources. We conducted long-term, continuous testing of temperature, humidity, light intensity, and CO2 concentration parameters in a heliostat greenhouse in the central and western parts of the Inner Mongolia Autonomous Region, a cold and arid region of northern China. A large amount of data was processed by statistical observation, simulation analysis, and 3D reconstruction to obtain the overall distribution, variation pattern, and mathematical model of the regional greenhouse environment in time and space. The results show that the temperature, humidity, light intensity, and CO2 concentration in the greenhouse have significant daily variation patterns, that there are strong coupling relationships between light intensity–CO2 concentration–time and indoor temperature–light intensity–CO2 concentration, that the coefficients of determination (R2) of the mathematical models are 0.88 and 0.89, and that the standard errors (RMSE) are 49.67 ppm and 45.30 ppm, respectively. The environmental parameters were fitted with high accuracy in order to provide scientific data for the cultivation of heliostats in the region.

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On Modeling Greenhouse Air-Temperature: an Experimental Validation

Cited by 3 publications

References 14 publications

Smart Greenhouse and Warehouse Monitoring with Disease Detection using Machine Learning

Smart Greenhouse and Warehouse Monitoring with Disease Detection using Machine Learning

A Takagi-Sugeno Fuzzy Model for Greenhouse Climate

Testing and Analysis on the Spatial and Temporal Distribution of Light Intensity and CO2 Concentration in Solar Greenhouse

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