Greenhouse Model Research Articles

Energy optimization and plant comfort management in smart greenhouses using the artificial bee colony algorithm

Agriculture is an essential component of human sustenance in this world. These days, with a growing population, we must significantly increase agricultural productivity to meet demand. Agriculture moved toward technologies as a result of the demand for higher yields with less resources. Increasing awareness of the significance and influence of agricultural practices in global climate change has made the use of energy-efficient innovations a vital aspect of the agriculture sector. The use of greenhouses to provide controlled environments that encourage effective plant growth is one of the current associated approaches. If not properly maintained, the energy used to run the greenhouses’ chillers, heaters, humidifiers, carbon dioxide (CO₂) generators, and carbon emissions becomes expensive. The goal of this research is to create a sustainable greenhouse model while achieving the best plant growth requirements with minimal use of energy. In order to achieve the lowest possible amount of energy consumption, the optimization model considered temperature, humidity, CO₂ levels, and sunlight as essential parameters in the environment. The Artificial Bee Colony (ABC) optimization technique was utilized for setting the environmental parameters for plant growth, considered for the suggested system. The system’s inputs were plant-preferred factors, and plant comfort was achieved by applying ABC to boost the parameters’ efficiency. A fuzzy controller was utilized to regulate different devices, including humidifiers, heaters, chillers, and CO₂ generators, by entering the introduced values. The overall efficacy of the fuzzy controllers that switch On/Off the actuators was obtained by minimizing the error between the best estimates of environmental factors and the ABC optimized values. Additionally, the suggested method was contrasted with other effective algorithms, such as Genetic Algorithm (GA), Firefly Algorithm (FA), and Ant Colony Optimization (ACO). Based on the results of the comparison analysis between the ABC algorithm and current practices, present procedures do not minimize the fluctuations in the inaccuracy between the target and actual environmental parameters, which is a necessary step towards increasing energy efficiency. The suggested method used 162.19 kWh for temperature control, 84.65405 kWh for Humidity, 131.2013 kWh for Sunlight, and 603.55208 kWh for CO₂ management, indicating the maximum energy efficiency. ACO needed 172.2621 kWh, 88.269 kWh, 175.7127 kWh, and 713.2125 kWh, in contrast to FA 169.7983 kWh, 86.04496 kWh, 155.8442 kWh, and 743.7986 kWh. Temperature, Humidity, Sunlight, and CO₂ were measured by GA at 164.1609 kWh, 86.19566 kWh, 174.6429 kWh, and 734.9514 kWh, respectively. In terms of Plant comfort, the suggested approach also outperformed 0.986770848 ACO (0.944043), FA (0.949832), and GA (0.946076). It is important to note that the research being done has the potential to minimize operating costs and maximize the amount of energy needed for plant growth, thereby creating a model for sustainable greenhouse agriculture.

Sustainable commercially-scaled greenhouse building cooling solution: Integrating PCM storage, desiccant wheels, and absorption chillers powered by dual-source solar/biomass energy

This research aims to address the critical need for sustainable cooling systems in greenhouses, particularly relevant in mitigating global warming impacts and enhancing food security worldwide. The urgency becomes more pronounced in locations experiencing high ambient temperature and humidity. The study introduces an innovative cooling system integrating Phase Change Material, a desiccant wheel, and an absorption chiller, powered by solar and biomass energy. This novel system aims to efficiently regulate temperature and humidity in greenhouse environments. The performance of this system is examined in Abu Dhabi, Doha, and Riyadh during the summer months, utilizing TRNSYS software for a medium-scale greenhouse model. Additionally, a comprehensive Life Cycle Assessment is carried out to quantify the environmental impacts of the proposed system. Results indicate that in Abu Dhabi, the system yields a Coefficient of Performance (COP) of 1.108, effectively maintaining indoor climate conditions. Similarly, Doha and Riyadh exhibit COPs of 1.015 and 0.827, respectively. In terms of solar energy utilization, Abu Dhabi demonstrates a solar fraction of 40.4, corresponding to the lowest Global Warming Potential (GWP) at 0.106 kg CO2eq per 1 kW of provided cooling capacity. Conversely, Riyadh records the highest GWP at 0.149 kg CO2eq, followed by Doha at 0.118 kg CO2eq. The Energy Payback Time (EPBT) for the system in Abu Dhabi is calculated to be 3.96 years, the shortest among the examined cities. In comparison, Doha and Riyadh present longer EPBTs of 4.48 and 5.83 years, respectively. These findings suggest that the proposed system offers a viable and environmentally friendly alternative to conventional greenhouse cooling approaches.

Study on the Natural Ventilation Model of a Single-Span Plastic Greenhouse in a High-Altitude Area

The natural ventilation model plays a crucial role in greenhouse environmental control. It has been extensively studied by previous researchers, but it is limited to low-altitude areas. This study established a numerical model of single-span plastic greenhouses in high-altitude areas. The model was validated using measured data, showing a good agreement between the measured and simulated values. By setting boundary conditions based on on-site monitoring data, ventilation rates were extracted under different conditions for numerical simulations. Through nonlinear fitting, an empirical formula for natural ventilation rates, with a determination coefficient (R2) of 0.9724, was derived. The formula was validated through an energy balance analysis of indoor air. Different ventilation opening sizes were simulated to derive an empirical formula for natural ventilation rates based on opening size. Building on this, the relationship between plant height and ventilation rate was analyzed. As the dominant factors of natural ventilation change with environmental fluctuations, this study also proposed the threshold wind speed for wind pressure ventilation, thermal pressure ventilation, and coupled ventilation, filling the knowledge gap in relevant ventilation rate calculations. This is the first time that a natural ventilation model of single-span plastic greenhouses in high-altitude areas has been proposed, providing the basis in terms of modeling for the further development of local facility agriculture.

A spatio-temporal methodology for greenhouse microclimatic mapping

Greenhouse internal microclimate has been proven to be non-homogeneous in many aspects. However, this variability is only sometimes considered by greenhouse models, which often calculate climatic variables without any spatial reference. Farmers, on the other hand, may wish to have these differences highlighted as they could lead to aimed actions only for a specific area of the greenhouse, while at the same time, they are not willing to invest in sensors to be installed everywhere. This paper presents a data-driven methodology to generate a virtual 2D map of a greenhouse, which allows farmers to control any critical parameter they desire with minimum investment, as monitoring is done via soft sensing with only a few actual sensors. The proposed flow starts with a set of temporary sensors placed in the points of interest; then, a model for each of them is developed via linear regression and, finally, a map of the entire area can be derived by interpolating values from these models. This allows the generation of accurate models at a reduced cost as temporary sensors can be reused at other locations. The methodology has been tested on adjacent greenhouses and in two farms, where temperature and other climatic variables have been monitored. Experimental results show that the proposed methodology can reach an adjusted R2 value of 98% for predicting values in different greenhouse locations.

Luminescent Solar Concentrators for Greenhouse Applications Based on Highly Luminescent Carbon Quantum Dots

Carbon quantum dots (CQDs) are promising luminophores for luminescent solar concentrators (LSCs) in transparent photovoltaic greenhouse covers due to their high ultraviolet (UV)‐light absorption coefficient, which is vital for plant growth. Herein, high quantum yield (75%) and large Stokes shift (0.706 eV) CQDs are synthesized by a simple, fast, cheap, and mass scalable method. A comprehensive study on the LSC engineering is carried out. Thin layers of CQDs with different concentrations of 1, 3, and 5 wt% and different number of layers (1–5) are coated on glass and poly(methyl methacrylate) (PMMA) waveguides, sized 5 × 5 × 0.6 and 15 × 15 × 0.6 cm3. The best performing single‐layer LCS exhibits power conversion efficiency (PCE) and optical efficiency as high as 1.6% and 6.5%, respectively (LSC size 5 × 5 × 0.6 cm3), and 1.19% and 3.27% (LSC size of 15 × 15 × 0.6 cm3), respectively. Over 90 days, stability tests show a 2% PCE decrease. Tests on a small‐scale greenhouse model demonstrate that transparent photovoltaic LSC roofs not only produce electricity but also control temperature inside the greenhouse. Hence, CQD‐based LSCs synthesized by the scalable method can be used in commercialization of transparent greenhouses photovoltaic covers.

Investigation on the effect of operation of solar dryer technology according to the drying characteristic of dried product

The application of solar dryer through greenhouse (GH) model as drying method for crops-based product is extremely important in industrial scale agriculture, especially in developed country. The drying model is considered environmentally friendly since it utilizes solar power as the main drying energy. However, the fundamental operational aspect of the basic GH is less discussed according to the drying mechanism of the dried product. In this work, three small-scale drying model are evaluated: open drying (OD), natural convection GH (NCGH), and forced convection GH (FCGH). The drying product is potato, which can be taken as an essential agricultural product in modern world. The OD model has the lowest mass loss rate which less than 50 % of the water from product can be evaporated. The operation of both GH is satisfactory, resulting more than 60 % moisture evaporation. The drying characteristic indicates the two-step failing rate which makes the mass loss fluctuation from the process. The highest mass loss is observed during the constant rate period around 21 % for FCGH and 18 % for NCGH. In addition, the detailed analysis on the effect of each process is discussed in this work. For example, the presence of hair-like structure and the movement of the pith of dried potato during the process. Also, a higher drying rate from NCGH and FCGH initiate the gelatinization and compartmentalization, resulting a substantial water evaporation of the product. It confirms the important correlation of the drying process and water diffusion mechanism of the dried product. The finding from this study can be taken as a vital reference for improving the operation of GH solar dryer

COMPARING THE ECONOMIC EFFECTS OF OPEN FIELD AND PROTECTED AREA ORGANIC TOMATO CULTIVATION SYSTEMS

To compare the economics of different systems of organic tomato production, two models were created, one assuming outdoor production, and the other representing production in a protected area under a greenhouse, based on the data obtained through interviews with organic tomato producers from Vojvodina. The cost and sensitivity analysis revealed that the greenhouse model yields better results overall (a financial result of €273/100 m2 compared to €58/100 m2), despite the higher costs due to amortization, interest and costs related to the higher yield obtained. The production model also showed less dependence on the change in organic tomato yield and price, as well as key cost groups and post-harvest losses, which in both cases were mediated by growing coriander as an intercrop. This research improves the knowledge of the economics of organic tomato cultivation and at the same time proposes a methodology to analyze the economic impact of other organic productions.

Optimized model predictive control for solar assisted earth air heat exchanger system in greenhouse

Agricultural greenhouses play a crucial role in addressing the threat of climate change to agricultural systems. The greenhouse systems control exhibits nonlinear characteristics and large lag, both affecting the regulation of the greenhouse thermal environment. In this paper, a control strategy was designed to set the dynamic reference value of the control objective of the greenhouse model, considering factors such as solar radiation and the heating capacity of water body. A complete greenhouse model was developed using TRNSYS, and then the model predictive control building and optimization were implemented through MATLAB. The control effects of traditional proportional-integral-differentiation control, initial model predictive control, and optimized model predictive control were compared. The results showed that the regulation of greenhouse temperature was improved by 10.6 % in the coldest mouth compared to the conventional proportional-integral-differentiation control by the optimized model predictive control. During a typical cold month, the violation of constraints was reduced by 29.7 % by dynamically setting the target of the greenhouse with the optimized model predictive control. The performance of greenhouse climate control is enhanced by the proposed optimized model predictive control method, ensuring a stable regulation of the crop growth environment.

VISIBLE WAVELENGTH EFFECT ON TEMPERATURE CHANGE IN GREENHOUSE EFFECT: LABORATORY DESIGN

School internships typically adhere to a standard format, employing basic tools for educational purposes. Among these, the greenhouse effect modelling laboratory, traditionally conducted under direct sunlight, faces challenges due to the variability introduced by cloud cover. This variability limits the ability to study the influence of light wavelength on the greenhouse effect, an aspect not accounted for when using sunlight alone. This research aims to explore the impact of light wavelength on temperature changes within greenhouse effect models. In our methodology, we employed an experimental setup that simulated the greenhouse effect using artificial light sources of varying wavelengths: red (641 nm), orange (592 nm), yellow (586 nm), green (536 nm), and blue (474 nm). The experiment involved monitoring the temperature increase within the model greenhouse under each light condition, thereby isolating the effect of wavelength from other environmental variables. The results revealed a direct correlation between light wavelength and the rate of temperature increase in the greenhouse model. Specifically, longer wavelengths were associated with a quicker rise in temperature, highlighting the significant role of wavelength in the greenhouse effect's efficiency. This study underscores the necessity of incorporating wavelength considerations into greenhouse effect models, particularly in educational settings. By integrating such experiments into school curricula, students can gain a deeper, more nuanced understanding of the greenhouse effect, moving beyond the limitations of traditional sunlight-based experiments.

CFD Analysis and Optimization of a Plastic Greenhouse with a Semi-Open Roof in a Tropical Area

A numerical simulation model of a natural ventilation greenhouse is helpful for improving the production and quality of greenhouse crops in tropical areas. Field experiments show that the mean coefficient of variation of indoor light intensity in four seasons was lower than 10.0%. The highest indoor temperature reached 39.3 °C during summer, while the average indoor temperature ranged from 24 °C to 26 °C in the other three seasons. The average relative humidity in the greenhouse ranged from 76% to 87% annually, which was higher and more stable than that in the external environment. A three-dimensional steady-state numerical model of the greenhouse was established based on computational fluid dynamics. Under natural ventilation conditions, the maximum error between the simulated value and the measured value of the temperature in each measuring point was 5.90%. And the average relative error between the simulated and measured values was 3.0% in the range of 0.7−1.5 m of crop cultivation height. Finally, a numerical simulation of adding side windows and expanding the vents was carried out. The results show that these methods can homogenize the airflow distribution in the greenhouse and improve the utilization efficiency of natural ventilation without more mechanical system operations.

Energy modeling, calibration, and validation of a small-scale greenhouse using TRNSYS

Greenhouse energy modeling is a prevalent tool for optimizing greenhouse energy consumption. However, for a model to serve its intended use, it is imperative to have a high level of confidence in the precision of its predictions. In this paper, a validated greenhouse energy model for a typical small-scale greenhouse in a cold climate is developed. The model is created using TRNSYS, a building performance simulation tool, with detailed energy modeling components and a user-defined crop model. The model is calibrated to fix uncertain parameters. A sensitivity analysis is used first to identify sensible uncertain parameters, followed by a multi-stage automated calibration. The automated calibration method uses a multi-objective genetic algorithm to adjust the uncertain parameters, calibrating the model for the measured indoor air temperature and relative humidity. The model performed well during the free-floating and ventilated stages (56 days) with a combined root mean square error (RMSE) of 1.6 °C for indoor air temperature and 8.3 % for the air relative humidity. The validation process involved assessing the applicability of the calibrated model using two additional datasets. For all the cases, comparing the simulation results with indoor environment measurements resulted in an RMSE of less than 2 °C for air temperature and less than 10 % for air relative humidity; these values compare favorably to the literature. The model achieved a 3.7 % mean relative error (MRE) in estimating monthly energy consumption for a minimally heated greenhouse. Given these results, the model is deemed sufficiently accurate and applicable for future investigations.

IoT-enabled Greenhouse Systems: Optimizing Plant Growth and Efficiency

Greenhouses have long been important in the advancement of agricultural operations because they provide regulated settings for optimal plant growth. With the introduction of real-time monitoring and automation capabilities, the Internet of Things (IoT) integration into greenhouse systems represents a revolutionary change. This abstract delves into the wider field of greenhouse technology, highlighting the role that IoT plays in improving agricultural in controlled environments. Conventional greenhouses provide plants with a protected environment, but they might not be as accurate or flexible. Intelligent control of environmental conditions is made possible by the introduction of IoT-enabled greenhouses, which utilize data exchange protocols, actuators, and sensors that are networked. The project aims to elevate traditional greenhouse models by integrating Node-RED and MQTT technologies. Transitioning from a Blynk-based prototype showcases the system's versatility. Other key components, including NodeMCU, sensors for real-time data, and LED lighting, collaborate to redefine controlled environment agriculture. The Raspberry Pi serves as a central hub, facilitating seamless communication through Node-RED and MQTT. This advanced greenhouse system harmonizes cutting-edge technologies, showcasing a commitment to sophistication and adaptability in agricultural practices.

Modelling Heat and Mass Transfer in High-Capacity Natural Convection Solar Dryers

Predicting solar dryer performance under different environmental conditions or assessing their performance to dry different grains is challenging since repeatable full-scale tests are expensive and time consuming. In the present study, computational fluid dynamics approach was used to model the drying of maize in high-capacity dryers such as greenhouse and solar bubble dryer. The absorption of short-wave radiation and the greenhouse effect in the dryer with incident solar radiation was modelled using a dual-band spectrum. The distribution of airflow, temperature, and absolute humidity was analysed in this study to optimise the drying process of maize. Additionally, these results were also used to quantify the drying rate of both greenhouse and solar bubble dryer. The greenhouse dryer model overpredicted the dryer temperatures by an average of 0.12%, and overpredicted absolute humidity by 0.38 per cent. The average Root-Mean-Square Error (RMSE) of temperature prediction was 1.8 °C, and the average RMSE for absolute humidity was 0.0042 for the greenhouse model. On the other hand, the solar bubble dryer model underpredicted temperatures by 1.7%, and underpredicted humidity values by 0.3 per cent. The mean absolute percentage error for the temperature and absolute humidity prediction of the solar bubble dryer model was 1.69% and 0.28%, respectively. The predicted and observed spatial variation in the temperature was similar for both dryers.

Experiment on the Uniformity of Internal Lighting in Zigzag Photovoltaic Greenhouses by Grating Plates

As one of the main projects of facility agriculture promotion, the PV (photovoltaic) greenhouse has the problems of PV power generation competing for light with crop production, strong indoor chiaroscuro, and uneven light distribution. The internal light uniformity is tested by a zigzag greenhouse model to compare the light transmission effects of different light-transmitting materials applied to PV greenhouses. Altogether, 20 line/inch 3 mm and 30 line/inch 3 mm, 40 line/inch 2 mm, 25 line/inch 4 mm grating plates and 2 mm and 3 mm thick ordinary glass were used as light-transmitting components, and the light intensity and light uniformity in the greenhouse were the measurement indicators. The results show that the use of grating plates as covering material can improve the light intensity at the intersection of light and dark, but the overall light transmittance is not as good as glass because it is plastic, which ages easily with low light transmittance. It can also improve the use of land under the shade of PV modules to provide a better growth environment for crops. The test results show that using grating plates can maximize the light intensity of the greenhouse and solve the problem of uneven distribution of light inside the greenhouse caused by obstruction of PV equipment and greenhouse framework. In sunny weather, the light intensity in three rows of the measurement points at the north side in the greenhouse is greater than 20,000 Lx, and the light environment in other areas is between 5000 Lx and 20,000 Lx, which is suitable for planting shade-loving crops.

Day-Ahead scheduling of rural integrated energy systems based on distributionally robust optimization theory

The integration of diverse industries in rural areas is an important step in building a smart countryside and achieving rural revitalization. Aiming at the problem of high energy consumption and pollution in agricultural production parks under the traditional energy structure, a new rural comprehensive energy park distribution robust scheduling model for water containing product breeding greenhouses is proposed in this paper. Firstly, a greenhouse model for aquaculture based on the principle of thermal flow is established. Secondly, considering energy production, consumption and storage, a rural integrated energy system model is established. In response to the uncertainty of residential electricity load, solar radiation, and environmental temperature within the park, a distributed robust optimization model is established using Kullback Leibler divergence. Finally, a numerical simulation is conducted using energy data from a real rural aquaculture park. The results show that compared to agricultural production parks under traditional energy models, the new rural comprehensive energy system model proposed in this paper fully integrates rural resources, reduces daily operating costs by 74.832 %, and reduces total carbon emissions by 33.75 %, providing theoretical support for rural industrial structure adjustment and energy transformation.

Environmental Prediction Model of Solar Greenhouse Based on Improved Harris Hawks Optimization-CatBoost

Solar greenhouses provide a favorable climate environment for the production of counter-seasonal crops in northern China. The greenhouse environment is a key factor affecting crop growth, so accurate prediction of greenhouse environment changes helps to precisely regulate the crop growth environment and helps to promote the growth of fruits and vegetables. In this study, an environmental prediction model based on the combination of a gradient boosting tree and the Harris hawk optimization algorithm (IHHO-Catboost) is constructed, and in response to the problems of the HHO algorithm, such as the fact that the adjustment of the search process is not flexible enough, it cannot be targeted to carry out a stage search, and sometimes it will fall into the local optimum to make the algorithm’s search accuracy relatively poor, an algorithm based on the improved Harris hawk optimization (IHHO) algorithm-based parameter identification method is constructed. The model considers the internal and external environmental and regulatory factors affecting crop growth, which include indoor temperature and humidity, light intensity, carbon dioxide concentration, soil temperature and humidity, outdoor temperature and humidity, light intensity, carbon dioxide concentration, wind direction, wind speed, and opening and closing of upper and lower air openings of the cotton quilt, and is input into a prediction model with a time series for training and testing. The experimental results show that the MAE (mean absolute error) values of temperature, relative humidity, carbon dioxide concentration, and light intensity of the model are reduced to 49.8%, 35.3%, 72.7%, and 32.1%, respectively, compared with LSTM (Long Short-Term Memory), which is a significant decrease in error. It shows that the proposed multi-parameter prediction model for solar greenhouse environments presents an effective method for accurate prediction of environmental data in solar greenhouses. The model not only improves prediction accuracy but also reduces dependence on large data volumes, reduces computational costs, and improves the transparency and interpretability of the model. Through this approach, an effective tool for greenhouse agriculture is provided to help farmers optimize the use of resources, reduce waste, and improve crop yield and quality, ultimately leading to a more efficient and environmentally friendly agricultural production system.

Estimating the Light Interception and Photosynthesis of Greenhouse-Cultivated Tomato Crops under Different Canopy Configurations

Understanding the spatial heterogeneity of light and photosynthesis distribution within a canopy is crucial for optimizing plant growth and yield, especially in the context of greenhouse structures. In previous studies, we developed a 3D functional-structural plant model (FSPM) of the Chinese solar greenhouse (CSG) and tomato plants, in which the greenhouse was reconstructed as a 3D mockup and implemented in the virtual scene. This model, which accounts for various environmental factors, allows for precise calculations of radiation, temperature, and photosynthesis at the organ level. This study focuses on elucidating optimal canopy configurations for mechanized planting in greenhouses, building upon the commonly used north–south (N–S) orientation by exploring the east–west (E–W) orientation. Investigating sixteen scenarios with varying furrow distance (1 m, 1.2 m, 1.4 m, 1.6 m) and row spacing (0.3 m, 0.4 m, 0.5 m, 0.6 m), corresponding to 16 treatments of plant spacing, four planting patterns (homogeneous row, double row, staggered row, incremental row) and two orientations were investigated. The results show that in Shenyang city, an E–W orientation with the path width = 0.5 (furrow distance + row distance) = 0.8 m (homogeneous row), and a plant distance of 0.32 m, is the optimal solution for mechanized planting at a density of 39,000 plants/ha. Our findings reveal a nuanced understanding of how altering planting configurations impacts the light environment and photosynthesis rate within solar greenhouses. Looking forward, these insights not only contribute to the field of CSG mechanized planting, but also provide a basis for enhanced CSG planting management. Future research could further explore the broader implications of these optimized configurations in diverse geographic and climatic conditions.

РАЦИОНАЛЬНЫЕ КОНСТРУКТИВНЫЕ РЕШЕНИЯ КРУГЛОГОДИЧНЫХ АНГАРНЫХ ТЕПЛИЦ

The subject of the study is year–round greenhouses of the hangar type for individual and farm farms. The costs of the construction and operation of greenhouses significantly affect the cost of agricultural products grown in them. Optimization of the design solutions of hangar greenhouses is important to reduce internal forces in the frame elements, reduce the material consumption and cost of greenhouses. The purpose of the study is to determine the optimal design solution for hangar greenhouses up to 6 m wide. The study was conducted with the help of the Russian certified software package "Lira-CAD". Calculated models of hangar greenhouses with arched, gable-roofed and lancet-shaped greenhouses with a width of 3 to 6 m have been developed. The design solutions of the greenhouses were developed taking into account the requirements of the code of rules SP 107.13330 "Greenhouses and greenhouses", based on the possibility of using polycarbonate sheets in enclosing structures. Comparative calculations of greenhouse structures in the Lira-CAD software package have been performed. The calculation results are analyzed. Conclusions have been drawn on optimizing the design solutions of hangar greenhouses up to 6 m wide. The study showed that among the considered types of greenhouses, the most rational in terms of metal consumption are lancet-shaped greenhouses. The specific consumption of metal in lancet greenhouses is 42..53% lower than in greenhouses with an arched roof, and 34...64% lower compared to greenhouses with a pitched roof. The specific consumption of metal decreases with an increase in the width of greenhouses, the greatest decrease in the specific consumption of metal is observed in greenhouses with a pitched roof. The most technologically advanced from the point of view of manufacturing and installation are greenhouses with a pitched roof, the manufacture of the frame of such greenhouses from galvanized steel pipes is possible in building conditions, and both polycarbonate and glass can be used for translucent fences.

A PID-based control architecture for mobile robot path planning in greenhouses

This article presents a control strategy for an Ackermann mobile robot designed for greenhouse operation, based on a cascade control mechanism. It controls both, (i) an inner loop, based on PID, for two motors, and (ii) a classic Pure Pursuit control for the outer loop. In addition, a feedforward control strategy is proposed to address the issue of the natural variable terrain in greenhouses in southern Spain. Path following will be evaluated using a 2D model of the experimental greenhouse to obtain coordinates that will be assessed in simulation. Open-loop tests were conducted on a real agricultural robot prototype, AGRICOBIOT II, designed and built at the University of Almeria.

Model of the control object of the mechatronic microclimate system of a medium-sized greenhouse

Sudden changes in air temperature and humidity have a negative impact on crop production. Modern methods of regulating the microclimate of greenhouse facilities are reduced to the simplest one - controlling the flow and temperature of air masses. The aim of this work is to create and test (verify the plausibility) a simplified model of the microclimate of a medium-sized greenhouse. The simplified greenhouse model takes into account the main processes that occur under the influence of external factors (air exchange, mass transfer, moisture transfer, heat transfer), and also takes into account the geometric and spatial characteristics of the object. Each test experiment involves determining the effect of only one parameter at fixed values of all other parameters. A simplified reference model of changes in microclimate parameters (temperature, air velocity and pressure) was developed using Ansys software. Using computer modelling of temperature fields and velocities, an analysis was carried out to determine the possibility of using the model in the control system. The microclimate characteristics were analysed when air pressure, velocity and temperature were stabilised. The results of the study and the developed model are suitable for use in control algorithms for the greenhouse mechatronic system to take into account cyclic daily changes in parameters.