Sensor Systems for Greenhouse Microclimate Monitoring and Control: a Review

Bhujel, Anil; Kumar, Basak Jayanta; Khan, Fawad; Arulmozhi, Elanchezhıan; Jaihuni, Mustafa; Sihalath, Thavisack; Lee, Deoghyun; Park, Jaesung; Kim, Hyeon Tae

doi:10.1007/s42853-020-00075-6

Cited by 31 publications

(10 citation statements)

References 87 publications

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“…In the greenhouse, the majority of its energy expenses (70-85%) are tailored toward temperature control. To measure the temperature in the greenhouse structure, the temperature sensors are to be installed spatially over the plants [13].…”

Section: Literature Reviewmentioning

confidence: 99%

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IoT-Based Greenhouse Monitoring and Control System

Oguntosin¹,

Okeke²,

Adetiba³

et al. 2023

IJCDS

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In conventional farming, farmers have to go around the farmland physically frequently to estimate the various environmental parameters like temperature, humidity, light intensity and soil moisture to harvest the ready crops at the appropriate time in the best soil possible. Although this conventional farming technique has been utilized for many years, it is irregular and fails to exhibit a high productivity rate because farmers sometimes cannot precisely assess all of the environmental parameters. Greenhouse farming, on the other hand, is a technique whereby the farmers grow crops in ecosystem habitats where all environmental factors are modified to suit the crop type. Automation in a greenhouse is a technology through which farmers may monitor and regulate the greenhouse environment automatically from anywhere in the globe at any time. This work aims to develop an automated greenhouse monitoring and controlling system, which integrates multiple sensors such as a temperature sensor, humidity sensor, light-dependent resistor sensor, and soil moisture sensor to obtain potential environmental parameters of the greenhouse, as well as integrate ESP32 development board, to store, process data and provide WiFi functionality. With the help of the Light Dependent Resistor (LDR), Temperature and humidity sensor and soil moisture sensor, the lighting of the bulb, fan activation and pump triggering can be controlled, respectively, whenever the environmental parameters are below the threshold value. Furthermore, with the help of the WiFi capability of the ESP32 development board, the Internet of Things (IoT) is utilized to store data in a database, process the acquired data, and eventually deliver the information to a user's web application for monitoring the environmental parameters in the greenhouse..

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Section: Literature Reviewmentioning

confidence: 99%

“…Nowadays, some modules have temperature and humidity sensors, which help with system simplicity. Below is a table showing detail of some of the humidity sensors used for humidity monitoring [13].…”

Section: Literature Reviewmentioning

confidence: 99%

IoT-Based Greenhouse Monitoring and Control System

Oguntosin¹,

Okeke²,

Adetiba³

et al. 2023

IJCDS

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“…In this regard, it is important to note that some farmers use monitoring devices, such as commercial meteorological stations. However, these stations are equipped and designed with a limited number of sensors that only measure the evolution of the main climate variables [ 14 ]. Therefore, even with such monitoring systems, the state of vent opening is often not recorded unless a control system is used for ventilation or specific sensors are installed in the vents.…”

Section: Introductionmentioning

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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“…This is done to improve monitoring and control of micro-climate parameters and sometimes facilitate remote-controlled and autonomous cultivation. Decisions may be made based on various actuators used to regulate heating, lighting, cooling, dosing of CO 2 and fertilizers, dehumidification, irrigation, screening, fogging, as examples ( Nelson, 1991 ; Uyeh et al, 2019 , 2021 ; Bhujel et al, 2020 ; Gadekallu et al, 2021 ). These actuators operate based on sensors providing feedback on measured data for the control loop set points configured in a computing device ( Stanghellini, 2013 ; Graamans et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

“…Multiclass models have been used to develop multivariate statistical methods in agriculture ( Gadekallu et al, 2021 ) and principal whale optimization-based neural networks to classify diseases in plants ( Gadekallu et al, 2021 ). Others include algorithms and systems for improved decision-making and optimizations ( Park et al, 2019 ; Syed and Hachem, 2019a , b ; Uyeh et al, 2019 , 2021 ; Bhujel et al, 2020 ). Using an inadequate number of sensors may lead to under-performance, while a likely result of being superfluous is large sizes of redundant data and its associated management problems.…”

Section: Introductionmentioning

confidence: 99%

Grid Search for Lowest Root Mean Squared Error in Predicting Optimal Sensor Location in Protected Cultivation Systems

et al. 2022

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Irregular changes in the internal climates of protected cultivation systems can prevent attainment of optimal yield when the environmental conditions are not adequately monitored and controlled. Key to indoor environment monitoring and control and potentially reducing operational costs are the strategic placement of an optimal number of sensors using a robust method. A multi-objective approach based on supervised machine learning was used to determine the optimal number of sensors and installation positions in a protected cultivation system. Specifically, a gradient boosting algorithm, a form of a tree-based model, was fitted to measured (temperature and humidity) and derived conditions (dew point temperature, humidity ratio, enthalpy, and specific volume). Feature variables were forecasted in a time-series manner. Training and validation data were categorized without randomizing the observations to ensure the features remained time-dependent. Evaluations of the variations in the number and location of sensors by day, week, and month were done to observe the impact of environmental fluctuations on the optimal number and location of placement of sensors. Results showed that less than 32% of the 56 sensors considered in this study were needed to optimally monitor the protected cultivation system’s internal environment with the highest occurring in May. In May, an average change of −0.041% in consecutive RMSE values ranged from the 1st sensor location (0.027°C) to the 17th sensor location (0.013°C). The derived properties better described the ambient condition of the indoor air than the directly measured, leading to a better performing machine learning model. A machine learning model was developed and proposed to determine the optimal sensors number and positions in a protected cultivation system.

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Sensor Systems for Greenhouse Microclimate Monitoring and Control: a Review

Cited by 31 publications

References 87 publications

IoT-Based Greenhouse Monitoring and Control System

IoT-Based Greenhouse Monitoring and Control System

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

Grid Search for Lowest Root Mean Squared Error in Predicting Optimal Sensor Location in Protected Cultivation Systems

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