Real-time adaptation of a greenhouse microclimate model using an online parameter estimator based on a bat algorithm variant

Guesbaya, Mounir; García-Mañas, Francisco; Megherbi, Hassina; Rodríguez, Francisco

doi:10.1016/j.compag.2021.106627

Cited by 15 publications

(1 citation statement)

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“…It is essential for IoT platforms as decision support systems that provide recommendations to farmers after analyzing the data measured by the stations installed in greenhouses [ 8 ]. Such recommendations are based on predictive models of greenhouse climate and crop growth [ 3 , 15 ], which require as input the state of vent opening to quantify the relevant effect of the ventilation flux (airflow rate) on the inside climate and crop [ 16 , 17 ]. In addition, continuous recording of the state of vent opening could also be important for legal aspects.…”

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confidence: 99%

A Soft Sensor to Estimate the Opening of Greenhouse Vents Based on an LSTM-RNN Neural Network

Guesbaya

García-Mañas

Rodríguez

et al. 2023

Sensors

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In greenhouses, sensors are needed to measure the variables of interest. They help farmers and allow automatic controllers to determine control actions to regulate the environmental conditions that favor crop growth. This paper focuses on the problem of the lack of monitoring and control systems in traditional Mediterranean greenhouses. In such greenhouses, most farmers manually operate the opening of the vents to regulate the temperature during the daytime. Therefore, the state of vent opening is not recorded because control systems are not usually installed due to economic reasons. The solution presented in this paper consists of developing a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) as a soft sensor to estimate vent opening using the measurements of different inside and outside greenhouse climate variables as input data. A dataset from a traditional greenhouse located in Almería (Spain) was used. The data were processed and analyzed to study the relationships between the measured climate variables and the state of vent opening, both statistically (using correlation coefficients) and graphically (with regression analysis). The dataset (with 81 recorded days) was then used to train, validate, and test a set of candidate LSTM-based networks for the soft sensor. The results show that the developed soft sensor can estimate the actual opening of the vents with a mean absolute error of 4.45%, which encourages integrating the soft sensor as part of decision support systems for farmers and using it to calculate other essential variables, such as greenhouse ventilation rate.

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