Greenhouse Humidity Research Articles

Smart Glazing for Energy- and Cost-Efficient Greenhouse Humidity Regulation

Smart Glazing for Energy- and Cost-Efficient Greenhouse Humidity Regulation

Intelligent Regulation of Temperature and Humidity in Vegetable Greenhouses Based on Single Neuron PID Algorithm

Intelligent Regulation of Temperature and Humidity in Vegetable Greenhouses Based on Single Neuron PID Algorithm

A machine learning method based on stacking heterogeneous ensemble learning for prediction of indoor humidity of greenhouse

Efficient production management, high productivity, and improved product quality are essential for the success of greenhouse production in producing sustainable agricultural products. Several environmental factors, including air temperature, humidity, CO2 levels, and light levels, have a major influence on this. Managing internal humidity is critical to preventing climate variation, disease, and pests in glasshouses that can cause significant damage if not properly controlled. This article assesses the performance of machine learning models in predicting indoor humidity levels in a greenhouse using a dataset from Guilan University's greenhouse located in Rasht City, Iran. Seven regression models were used to make predictions: multiple linear regression (MR), polynomial regression (PR), decision tree regression (DT), k-nearest neighbors regression (KNN), support vector regression (SVR), random forest regression (RF), and extreme gradient boosting regression (XGBoost). Evaluation criteria including coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were used to evaluate each model. The best machine learning models were selected based on these criteria values (R2 > 0.94) and combined using the stacking method, a popular ensemble learning technique, to create a metamodel for accurately predicting internal humidity within the greenhouse. The metamodel showed exceptional performance, with significantly improved evaluation criteria on the test dataset, specifically R2 of 0.96515, MAE of 0.01395, MSE of 0.03205, and RMSE of 0.00102.

A practical solution for multivariable control of temperature and humidity in greenhouses

This paper presents a simple and effective control solution to regulate temperature and humidity inside a greenhouse, which are the main climatic variables affecting crop growth and health. A multivariable control strategy with inverted decoupling is proposed in two schemes. The nighttime control scheme uses heating and dehumidification, and the daytime control scheme uses natural ventilation, dehumidification, and humidification. A system identification methodology is used to obtain low-order linear models from data measured in a greenhouse. This simplifies the design of proportional–integral (PI) controllers and allows the use of well-known control techniques. For instance, anti-windup is used to deal with the saturation of the actuators and for a bumpless transfer mechanism to switch between the nighttime and the daytime control schemes. Setpoint weighting is used for the humidity controllers to attenuate undesired effects on the temperature control loop when the humidity setpoint is changed. Furthermore, split range control is used for the first time to control humidity by combining a heat-pump dehumidifier and a fogging system. The control solution presented in this work allows the integral absolute error to be reduced, on average, by 88.5% for the interactions affecting the temperature control loop, and by 85.6% for the humidity control loop, compared to decentralized PI control. The control schemes have been tested in simulation with real disturbance data from a Mediterranean greenhouse and in preliminary form in a real facility to conclude that the proposed multivariable control strategy could be effective in decoupling the temperature and the humidity control loops.

Multistep ahead prediction of temperature and humidity in solar greenhouse based on FAM-LSTM model

Solar greenhouses offer favorable climatic environments for the production of off-season crops in northern China. Greenhouse temperature and humidity are critical environmental factors influencing crop growth; therefore, it is crucial to predict the temperature and humidity. In this research, we constructed a feed-forward attention mechanism- long-short term memory (FAM-LSTM) model for the multistep prediction of temperature and humidity in a solar greenhouse. The FAM-LSTM model considered both internal and external environmental factors influencing crop growth, where the factors include temperature, humidity, light, soil temperature, and soil moisture. Then, we performed corresponding experiments employing this model in predicting both temperature and humidity at prediction horizons of 12, 24, 36, and 48 h. The FAM-LSTM model stands out due to its high accuracy compared with different models. The temperature prediction error of the model at four prediction horizons is primarily within [−0.5, 0.9], [−2.1, 1.7], [−0.9, 2.7], and [−1.8, 2.9] ℃, respectively. The humidity prediction errors of the model at four prediction horizons are mainly within [−1.9, 1.8], [−4.4, 3.6], [−6.9, 2.4], and [−5.8, 4.4] %, respectively. This study offers an effective temperature and humidity prediction approach for precise environmental regulation and damage warnings in solar greenhouses.

Flexible humidity sensor for smart agricultural applications

In order to meet the increasing food demand, sensors that measure the ambient temperature and humidity in greenhouses are needed for more efficient vegetable and fruit production. For this purpose, two different flexible and resistant textile-based humidity sensors that can measure the humidity at higher levels (80%, 90%, and 100% relative humidity (RH)) were designed and printed directly on the four different greenhouse fabrics using silver and carbon conductive inks. Depending on the humidity value in the environment, the sensor performance was tested based on sensor electrical resistance measurements with respect to repeated bending/cyclic tests, rubbing, ultraviolet (UV) exposure/ weatherability (against UV and raining) tests in order to simulate greenhouse conditions for smart agriculture. Despite applying 1024 bending cycles, up to 20 rubbing cycles, and 10 times UV and rain exposure to the humidity sensors at high RH, no significant change was detected in the resistance values of the humidity sensors. Moreover, some important features of the sensors such as hysteresis, repeatability, response time have been also examined. According to hysteresis test results, humidity sensors show acceptable dynamic response and response time of the sensors are 15.8, 17.3, 24.8, and 25 s at 100% RH for G2S, W1S, B2C, and W1C, respectively. Statistical analyses showed that the sensor designs and type of conductive inks had significant effects on the performance of the humidity sensors and the best sensor performance was obtained with the polypropylene coated fabric using design II and silver based conductive ink. The fabricated textile based flexible humidity sensors detect the change in RH levels from 80% RH to 100% RH and achieve good durability, and repeatability even after prolonged UV exposure and raining. Thus, the developed textile-based flexible humidity sensor might be useful for future smart agricultural applications.

Evaluation of a 0.7 kW Suspension-Type Dehumidifier Module in a Closed Chamber and in a Small Greenhouse

Controlling humidity inside greenhouses is crucial for optimum plant growth and controlling physiological disorders and diseases. The humidity response and uniformity depend extensively on the evaluation of the dehumidifier. The objective of this research was to evaluate a low-powered suspension-type dehumidifier module in terms of humidity changes and spatial and vertical variability in a closed chamber and in a small greenhouse. A wireless sensor network including 27 sensor nodes was used to collect the data during the humidity changes from 80% to 70% and 90% to 70%. The humidity response results showed that the times required for dehumidification from 80% to 70% and 90% to 70% were 13.75 and 21.51 min, respectively, for the closed-chamber operation. Similarly, for the small greenhouse, 18 and 35 min were required to reduce the humidity levels from 80% to 70% and 90% to 70%, respectively. The spatial and variability results indicated that the changes in humidity at the rear and bottom layers were slower than those in the other layers of both experimental areas. The findings of this study would aid in the development of dehumidification strategies and sustainable agriculture for monitoring and controlling humidity in greenhouses using low-powered dehumidifiers.

Predictive Model of Humidity in Greenhouses through Fuzzy Inference Systems Applying Optimization Methods

Establishing the indoor and outdoor humidity values in a greenhouse allows us to describe the crop yield during its entire developmental cycle. This study seeks to develop a predictive model of indoor relative humidity values in a greenhouse with high accuracy and interpretability through the use of optimized fuzzy inference systems, in order to offer greenhouse users a clear and simple description of their behaviour. The three-phase methodology applied made use of descriptive statistics techniques, correlation analysis, and prototyping paradigm for the iterative and incremental development of the predictive model, validated through error measurement. The research resulted in six models which define the behaviour of humidity as a result of temperature, CO2, and soil moisture, with percentages of effectiveness above 90%. The implementation of a Mamdani-type fuzzy inference system, optimized by a hybrid method combining genetic and interior point algorithms, allowed to predict the relative humidity in greenhouses with high interpretability and precision, with an effectiveness percentage of 90.97% and MSE (mean square error) of 8.2e − 3.

Multivariable control of nighttime temperature and humidity in greenhouses combining heating and dehumidification

This work presents a multivariable control strategy to regulate nighttime temperature and relative humidity inside a greenhouse, using a pipe heating system and a dehumidification system. The greenhouse is modeled as a two-input two-output (TITO) process with a system identification methodology to calculate low-order linear models. Proportional-integral (PI) controllers with anti-windup are used, and an inverted decoupling scheme is adopted. The strategy was tested in simulation with real data. Results suggest that it is attractive for real implementation and show for the first time that the use of dehumidification systems can be applied as part of a combined control strategy.

Elevated CO2 concentration induces changes in plant growth, transcriptome, and antioxidant activity in fennel (Foeniculum vulgare Mill.).

Fennel (Foeniculum vulgare Mill.) is widely used to produce natural bio-materials. Elevated CO2 (eCO2) concentrations in the atmosphere improve the net photosynthesis of plants. The aim of the present study was to investigate distinct changes in fennel growth characteristics and phytonutrient contents under different CO2 concentrations. The effects of 400 and 800 ppm concentrations on plant growth and antioxidant activity were observed under hydroponics. Plant growth was improved by eCO2 concentrations. We also observed diverse changes in nutrient solution (pH, electrical conductivity, and dissolved oxygen) and environmental factors (temperature and humidity) in greenhouse under light or dark conditions. Electrical conductivity increased under dark and eCO2 conditions, whereas the pH decreased. Additionally, we performed transcriptome analysis and identified CO2-responsive differentially expressed genes. In the 800 ppm group, genes involved in photosynthesis and Karrikin response were upregulated whereas those involved in syncytium formation were downregulated. Four upregulated differentially expressed genes involved in flavonoid biosynthesis and total flavonoid content were relatively increased under the 800 ppm CO2 condition. In contrast, antioxidant activity, including total phenolic content, scavenging activity, ferric ion reducing antioxidant power, and reducing power were decreased in fennel under relatively high eCO2 concentrations. Moreover, different light intensities of 12 or 24 lx did not affect the growth and antioxidant activity of fennel, suggesting eCO2 has a stronger effect on plant improvement than light intensity. The results of the present study enhance our understanding of the positive effects of CO2 on the growth and antioxidant activity of fennel.

A Deep Learning Model to Predict Evapotranspiration and Relative Humidity for Moisture Control in Tomato Greenhouses

The greenhouse industry achieves stable agricultural production worldwide. Various information and communication technology techniques to model and control the environment have been applied as data from environmental sensors and actuators in greenhouses are monitored in real time. The current study designed data-based, deep learning models for evapotranspiration (ET) and humidity in tomato greenhouses. Using time-series data and applying long short-term memory (LSTM) modeling, an ET prediction model was developed and validated in comparison with the Stanghellini model. Training with 20-day and testing with 3-day data resulted in RMSEs of 0.00317 and 0.00356 kgm−2 s−1, respectively. The standard error of prediction indicated errors of 5.76 and 6.45% in training and testing, respectively. Variables were used to produce a feature map using a two-dimensional convolution layer which was transferred to a subsequent layer and finally connected with the LSTM structure for modeling. The RMSE in humidity prediction using the test dataset was 2.87, indicating a performance better than conventional RNN-LSTM models. Irrigation plans and humidity control may be more accurately conducted in greenhouse cultivation using this model.

First Report of Leaf Spot Disease caused by Nigrospora oryzae on Nicotiana tabacum in China.

Tobacco (Nicotiana tabacum) is an important economic crop and widely cultivated in rural areas in south of China. A previously uncharacterized disease was observed on field-grown tobacco during 2020 and 2021 around Tongren city, Guizhou province of China (27°59'25.73" N, 108°7'2.43" E). The disease mainly occurred from fast growing period (about 13-16 leaves) to leaf maturity stage. In severely diseased areas, the incidence rate was between 20%-100%. Symptoms first began as yellow-brown necrotic spots on leaves, then merged into larger irregular necrotic spots surrounded by chlorotic halos. Similar lesions were also found on the stems. Ten symptomatic leaf and stem samples were collected from the different infected plants for pathogen isolation. The small pieces of discolored tissues were surface-disinfected with 2% sodium hypochlorite for 3 min and 75% ethanol for 30 s, rinsed three times with sterile water, and blotted on sterile filter paper, placed on potato dextrose agar thenincubated at 28°C in the dark for 3-4 days. The obtained isolates were purified through single-spore culture. Colonies were initially white and fluffy in appearance, later turning gray. Hyphae were smooth, branched, septa, transparent or light brown. Spores were solitary, oblate or nearly spherical, dark brown to black, smooth, 14.3 to 16.1μm × 11.8 to 15.2 μm in diameter. DNA of fungal isolates were extracted using Fungi Genomic DNA Extraction Kit (Solarbio, Beijing, China), the internal transcribed spacer (ITS) of the ribosomal DNA, β-tubulin (TUB2) gene and translation elongation factor 1-alpha (TEF1-α) were amplified with primers ITS1/ITS4, βt2a/βt2b and EF1-1728F/EF1-986R, respectively. The resulting ITS, TUB2 and TEF1-α sequences were deposited at GenBank, NCBI under accessions MZ882151, MZ927749, MZ927747, respectively. The sequence identity of ITS, TUB2 and TEF1-α with those of Nigrospora oryzae strains HBN (KU254608), HGUP191068 (MZ724102) and LC7307 (KY019409) were 99.64%, 99.29% and 99.65%, respectively. Based on morphological features and phylogenetic analysis, the pathogen was identified as N. oryzae (Wang et al. 2017). Pathogenicity tests were conducted by placing agar plugs-containing fungal mycelia and agar blocks (control) on leaves of tobacco plants grown at 28°C with 60% humidity in greenhouse. Symptoms appeared on the pathogen inoculated leaves seven days after inoculation, whereas the control treatment remained symptomless. The pathogens were reisolated from diseased leaves and identified as N. oryzae based on morphological, molecular and phylogenetic analysis, which were fulfilling Koch's postulates. This pathogen was recently identified from watermelon and kiwifruit in the Guizhou (Far and Rossman, 2021). To our knowledge, this is the first report of leaf spot caused by N. oryzae on Nicotiana tabacum in China.

A CFD transient model of leaf wetness duration on greenhouse cucumber leaves

The estimation of leaf wetness duration (LWD) is important for crop disease monitoring and early warning, because LWD provides the necessary conditions for pathogen infection. Crop canopy condensation caused by high humidity in greenhouses is one of the main causes of LWD formation, and measuring LWD in greenhouses is difficult. A simulation model based on agricultural meteorological parameters is typically used to replace field measurements. This study was conducted in a Chinese solar greenhouse. A 2D transient model based on computational fluid dynamics (CFD) was used to estimate the distribution of cucumber leaf condensation in a solar greenhouse during early summer nights in Beijing. The LWD was estimated by considering the duration of the simulated leaf condensation at each point and simulating the dehumidification effect under ventilation conditions. The visual observations of leaf condensation were compared with the simulation results from May 31 to June 1 and from June 3 to June 4, 2021 (cloudy and clear days, respectively). The horizontal leaf condensation was observed first near the south roof, whereas the vertical canopy had a longer LWD at 1 m from the ground (average value of 8 h). LWD was estimated using relative humidity thresholds (RMSE of 1.944 h on cloudy days and RMSE of 0.5 h on clear days), and the good agreement between measurements and estimation indicated that the 2D CFD model combined with the relative humidity threshold method could be used to estimate the temporal and spatial distribution of canopy LWD.

Sensitivity analysis of the DehumReq model to evaluate the impact of predominant factors on dehumidification requirement of greenhouses in cold regions

In this study, the sensitivity of a novel dehumidification requirement model (DehumReq) is analyzed to evaluate the effect of the predominant factors on the dehumidification needs of the greenhouses. The hourly dehumidification demand and sensitivity coefficient (SC) are estimated for three different seasons: warm (July), mild (May), and cold (November), by using the local sensitivity analysis method. Based on SC values, the solar radiation, air exchange, leaf area index (LAI), and indoor setpoints (temperature, relative humidity (RH), and water vapor partial pressure (WVPP)) have significant impact on the dehumidification needs, and the impact varies from season to season. Most parameters have a higher SC in summer, whereas solar radiation and LAI have a higher SC in mild season. The dehumidification load increases 4 times of its base value with increasing solar radiation by 200 W/m2, and the highest LAI (10) caused 5 times increment of the load. The changing of WVPP from its base value (1.5 kPa) to maximum (2.9 kPa) reduces the load 70% in summer. Air exchange was found to be the most crucial parameter because it is the main dehumidification approach that has a large range and is easily adjustable for any greenhouses. Sufficient air exchange by ventilation or infiltration will reduce the dehumidification load to zero in May and November and minimizes it to only nighttime load in July. For the other parameters, higher ambient air RH and indoor air speed will result in higher the dehumidification load; whereas higher inner surface condensation will result in lower dehumidification load. The result of this study will assist in the selection of the most efficient moisture control strategies and techniques for greenhouse humidity control.

A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems

Cultivating in greenhouses constitutes a fundamental tool for the development of high-quality crops with a high degree of profitability. Prediction and control models guarantee the correct management of environment variables, for which fuzzy inference systems have been successfully implemented. The purpose of this review is determining the various relationships in fuzzy inference systems currently used for the modelling, prediction, and control of humidity in greenhouses and how they have changed over time to be able to develop more robust and easier to understand models. The methodology follows the PRISMA work guide. A total of 93 investigations in 4 academic databases were reviewed; their bibliometric aspects, which contribute to the objective of the investigation, were extracted and analysed. It was finally concluded that the development of models based in Mamdani fuzzy inference systems, integrated with optimization and fuzzy clustering techniques, and following strategies such as model-based predictive control guarantee high levels of precision and interpretability.

Effects of Temperature and Humidity on the Quality and Textural Properties of Melon Fruits During Development and Ripening

The quality of melon fruit depends on the environmental growth conditions. We evaluated the effects of temperature and humidity in the greenhouse on the quality and textural properties of melon fruit during development and ripening. The greenhouse humidity and air and soil temperatures were monitored over two growth seasons of three melon varieties. The average air and soil temperatures were higher in spring than in autumn, whereas average air humidity was lower in spring. The contents of free amino acid, soluble protein, and soluble sugar in melon fruit were significantly higher in spring than in autumn. The contents of sucrose phosphate synthase and sucrose synthase were also significantly higher in spring than in autumn. In both seasons, the contents of water-soluble pectin and ionic soluble pectin increased during fruit maturation, whereas the content of covalent soluble pectin decreased. The cellulose and hemicellulose contents of the three melon cultivars decreased to different degrees during ripening. The contents of ploygalacturonase, pectinesterase, and β-galactosidase first increased and then decreased over time. The cellulase activity in the three cultivars increased steadily and peaked at maturity, suggesting that cell wall-modifying enzymes play important roles in the transformation of fruit texture.

Embedded Monitoring Method of Greenhouse Environment based on Wireless Sensor Network

Aiming at the problem of low accuracy of greenhouse environmental monitoring, an embedded monitoring method of greenhouse environment based on wireless sensor network is studied. The embedded microprocessor S3C2410 is selected as the control chip of greenhouse environmental monitoring. The wireless sensor network is composed of wireless detection node, sink node and remote-control terminal. The temperature and humidity sensor, CO2 concentration sensor and light intensity sensor are set as the detection nodes of the wireless sensor network. Each detection sensor node is configured with wireless communication module to form a wireless sensor network to realize data communication of environmental monitoring. The distribution map method is selected to eliminate the missing data of greenhouse environmental monitoring data collected by wireless sensor network, and the weighted average method is used to fuse the monitoring data after eliminating the missing data to obtain the final greenhouse environmental monitoring results. The experimental results show that this method can effectively detect the environmental data such as temperature, humidity and light intensity of greenhouse, and the relative error of each data is less than 1%.

Wireless monitoring algorithm of intelligent greenhouse based on fuzzy control

The existing greenhouse monitoring algorithm has a long delay time, so it is unable to carry out effective remote greenhouse monitoring, therefore, a new wireless monitoring algorithm based on the fuzzy control technolog was put forward, which was able to remotely monitor the greenhouse temperature, humidity and illumination data in real time. Firstly, the overall framework of greenhouse monitoring algorithm was built, including fuzzy clustering algorithm and sensing layer devices. Secondly, the temperature-humidity sensors and light sensitivity sensors in the sensing layer devices were used to deeply mine and optimize the parameters of temperature, humidity and light intensity in current greenhouse, so as to ensure the stability of subsequent transmission. Meanwhile, the corresponding perceptual recognition layer and broadband access method were designed, and GPRS technology was used to feed back the data information to the monitoring data layer through temperature-humidity sensors and light sensitivity sensors. Moreover, UDP protocol was taken as the data core transmission protocol, and the adaptive protection design algorithm was proposed to ensure the most reasonable transmission of monitoring data, get the current monitoring data of temperature, humidity and illuminance. The experimental results show that the maximum delay time of the algorithm is 46 s, which is far lower than the traditional algorithm, and the delay time of temperature monitoring is also lower than the traditional algorithm. It is results show that the response delay of remote intelligent greenhouse monitoring algorithm is low and the overall monitoring effect is ideal. The purpose of monitoring temperature, humidity and illumination can be achieved.

Determining of Climatic Parameters Using CFD in Different Window Span in Naturally Ventilated Greenhouses

The aim of this study was to compare with the measured inner air temperature and relative humidity values and the simulated values determined with Computational Fluid Dynamics (CFD) in the naturally ventilated gable-roofed single glass greenhouse located North-South direction, having 45o and 90° window spans and under no cultivation.
 The measured values were recorded every 2 hours from 8 am to 18 pm using the relative humidity and air temperature sensors placed in 7 different locations. Measurements were made in case of the 45° and 90° window span openness. For CFD simulations, the SolidWorks 2011 software was used. The values of air temperature and relative humidity inside the greenhouse were simulated depending on the outside ambient conditions and structural and physical properties of greenhouse. Then, the measured values were compared with the simulated values and error rate for each sensors were determined.
 As a result, it was determined minimum error rates of the measured and simulated air temperature and relative humidity in greenhouses is 4.8% and 4.7%, respectively. The study showed that the CFD can be used as a powerful tool for determining inner climatic factors in naturally ventilated greenhouses.

Energy Minimisation in a Protected Cropping Facility Using Multi-Temperature Acquisition Points and Control of Ventilation Settings

Energy management in protected cropping is critical due to the high cost of energy use in high-tech greenhouse facilities. The main purpose of this research was to investigate the optimal strategy to reduce cooling energy consumption, by regulating the settings (opening/closing) of either vents or curtains during the day, at the protected cropping facility at Western Sydney University. We measured daily changes in air temperature and energy consumption under four treatments (open/closed combinations of vents and shade screens) and developed an optimal cooling strategy for energy management using multi-temperature acquisition points at different heights within a greenhouse compartment. The optimal treatment (vents open/curtains closed) reduced energy load at the rooftop, thereby maintaining a desirable plant canopy temperature profile, and reducing cooling energy. Daily energy consumption was lowest for vents open/curtains closed (70.5 kWh) and highest for vents closed/curtains open (121 kWh). It was also found that delaying the operation of opening and closing of vents and curtains until the plant canopy temperature reached 25 °C reduced cooling energy consumption and decreased heating energy consumption in the morning (e.g., 08:00 to 10:00). The estimated savings of 1.83 kWh per 1 °C cooling between the optimal (vents open/curtains closed) and least optimal (vents closed/curtains open) conditions had the potential for significant energy savings at 494 kWh per °C over a crop cycle of nine months in warm weather conditions. However, selection of the optimal cooling strategy utilising control of vents and curtains must also account for the impact from other greenhouse environmental factors, including light, humidity, and CO2 concentration, which may be crop specific.