Search Results (44)

The growth environment of corps requires necessary improvements by Chinese solar greenhouses with Pad–Fan Cooling (PFC) systems for reducing their high temperatures in summer. Although computational fluid dynamics (CFD) could dynamically display the changes in humidity, temperature, and wind speed in solar greenhouses, its computational efficiency and accuracy are relatively low. In addition, the use of PFC systems can cool down solar greenhouses in summer, but they will also cause excessive humidity inside the greenhouses, thereby reducing the production efficiency of crops. Most existing studies only verify the effectiveness of a single machine learning (such as ARMA or ARIMA) or deep learning model (such as LSTM or TCN), lacking systematic comparison of different models. In the current study, two machine learning algorithms and three deep learning algorithms were used for their ability to predict a PFC system’s cooling effect, including on humidity, temperature, and wind speed, which were examined using Auto Regression Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Time Convolutional Network (TCN), and Glavnoe Razvedivatelnoe Upravlenie (GRU), respectively. These results show that deep learning algorithms are significantly more effective than traditional machine learning algorithms in capturing the complex nonlinear relationships and spatiotemporal changes inside solar greenhouses. The LSTM model achieves R² values of 0.918 for temperature, 0.896 for humidity, and 0.849 for wind speed on the test set. TCN showed strong performance in identifying high-frequency fluctuations and extreme nonlinear features, particularly in wind speed prediction (test set R² = 0.861). However, it exhibited limitations in modeling certain temperature dynamics (e.g., T6 test set R² = 0.242) and humidity evaporation processes (e.g., T7 training set R² = −0.856). GRU delivered excellent performance, achieving a favorable balance between accuracy and efficiency. It attained the highest prediction accuracy for temperature (test set R² = 0.925) and humidity (test set R² = 0.901), and performed only slightly worse than TCN in wind speed prediction. In summary, deep learning models, particularly GRU, offer more reliable methodological support for greenhouse microclimate prediction, thereby facilitating the precise regulation of cooling systems and scientifically informed crop management. Full article

Crops in greenhouses located in cold climates are frequently affected by high relative humidity (RH). This study presents the design, testing, and analysis of a dehumidifier based on thermoelectric cooling. Thermoelectric dehumidifiers (TEDs) are capable of dehumidifying greenhouses in cold regions while recovering heat for indoor air heating. The design of a TED is based on the specific characteristics of thermoelectric coolers (TECs). A TED consists of a cabinet, four heat exchangers, a duct fan, a water pump, and auxiliary components. The TED performance was evaluated in a Chinese solar greenhouse (CSG) with a volume of approximately 160 m³. The input voltage of the TECs, fan airflow rate, and cold-side fin area affected the TED performance, with their influence varying in magnitude. The radar chart results show that the optimal operating parameters are as follows: a fan airflow rate of 300 m³/h, a TEC input voltage of 15 V, and a cold-side fin area of 0.15 m². With the TED running for 120 min under the optimal parameters, the RH in the CSG decreased by 25.5%, while the air temperature increased by 3.4 °C. The installation of the TED at the bottom of the CSG improved the growing environment of the crops, particularly in the vertical range between 0.2 m and 1.5 m height inside the greenhouse. These findings provide a valuable reference for applying thermoelectric cooling technology in the greenhouse field. Full article

To ensure a high crop profit in Chinese solar greenhouses (CSGs), it is crucial to effectively manage major environmental variables such as temperature, humidity, and CO₂ concentrations, among others, to mitigate harmful effects on crop growth. The objectives of this study were to assess the spatial, vertical, and temporal variability of major environmental variables in CSGs during summer, and to provide fundamental information that could facilitate the monitoring and control of environmental factors in CSGs. The experiments were conducted in two CSGs: one with crops and another without crops. The measured environmental variables included air temperature, humidity, CO₂ concentration, light intensity, and wind conditions. Significant variations in the spatial, vertical, and temporal distribution of environmental factors were observed in both greenhouses. The results revealed significant diurnal patterns in temperature and humidity, with higher daytime temperatures and lower humidity levels. The greenhouse with crops exhibited warmer bottom layers due to restricted air mobility. CO₂ concentrations peaked at night, aligning with plants’ respiration and photosynthesis cycles, whereas light intensity showed substantial daytime peaks, slightly affected by the presence of crops. The study emphasized the necessity of stratified control of the environment and dynamic management of CO₂. The deployment of a wireless sensor network (WSN) and placement of an error-based sensor ensured precise monitoring, highlighting the importance of continuous data collection and adaptive management for optimal greenhouse conditions. Full article

Chinese Assembled Solar Greenhouses (CASGs) in the Gobi Desert region face significant diurnal temperature variations, with excessively high temperatures during the day and low temperatures at night, which adversely affect crop growth. Traditional temperature regulation technologies are hindered by high energy consumption, high costs, and severe pollutants. To address these issues, this study designed a heating system suitable for CASGs in the Gobi Desert region, integrating solar air source heat pump technology with underground pipe systems. The power consumption and performance of the system were assessed by comparing temperature and humidity in an experimental greenhouse (with the system), a control greenhouse (without the system), and outdoor environments under various typical climate conditions. The results indicated that the system exhibited excellent performance in both daytime heat absorption and nighttime heat release. Specifically, during operation, the maximum daytime temperature in the experimental greenhouse was reduced by up to 5 °C, while the minimum nighttime temperature increased by up to 8 °C, effectively preventing crop frost damage. The system achieved heat absorption rates of 14 to 16 KJ s⁻¹ and heat release rates of 36.5 to 37.5 KJ s⁻¹, with average coefficients of performance (COP) of 4.33 and 4.81. Compared to traditional heating methods using coal, gas, and electricity, the system reduced energy consumption by 84.7%, 81.3%, and 79.1%, respectively, and decreased greenhouse gas emissions by 8.24 t, 6.52 t, and 5.67 t, respectively. This system exhibits outstanding thermal efficiency, energy savings, and environmental benefits, while also showing promising economic benefits with a payback period of four years, providing a reliable heating solution for CASGs in the Gobi Desert region. Full article

This paper experimented with a methodology of machine learning modelling using virtual samples generated by fast CFD (Computational Fluid Dynamics) simulations in order to predict the greenhouse natural ventilation. However, the output natural ventilation rates using fast two-dimensional (2D) CFD models are not always consistent with the three-dimensional (3D) one for all the scenarios. The first contribution of this paper is a proposed comparative modelling methodology between two-dimensional and three-dimensional CFD studies, regarding its validity, especially when buildings are in rows. The results show that the error of the ventilation rate prediction could exceed 50%, if 2D models are not properly used. Subsequently, in those scenarios where the 2D and the 3D models had equal accuracy, nearly one thousand samples were generated using fast 2D CFD simulations to train a natural ventilation rate regression tree model. This model is efficient to deal with the combined effect of wind pressure and thermal gradients under various vent configurations, with only four necessary inputs. In addition, by analyzing the wind speed distribution contour of the outdoor wind field around the greenhouse rows, the optimal wind speed-measuring locations were determined to eliminate interference for predicting the natural ventilation rate. Full article

Chinese-style greenhouses (CSGs), characterized by a distinct geometric shape compared to traditional greenhouses, are extensively utilized in China. In this study, this type of greenhouse was modeled using TRNSYS software version 18 and experimentally validated. The model can transiently determine the indoor conditions of the greenhouse and the requirement for additional heating. It calculates the heat loss due to plant evapotranspiration as well as all the heat gains and losses from the surfaces. The application of this greenhouse has been investigated from the southernmost to the northernmost regions of Europe. For this purpose, cities located at different latitudes (between 32.63° N and 69.65° N) were entered into the model, and the results were obtained and compared. The analysis conducted over the entire year demonstrated that the CSG indoor temperature is more dependent on solar energy during the day and on outdoor temperature at night. The two southernmost cities in our survey, Funchal, Portugal (32.63° N) and Luqa, Malta (35.83° N), had no winter heating requirement. The thermal covering was sufficient to minimize night heat loss and maintain a suitable indoor temperature. In northern cities, the heating requirement was relatively high due to the lower outdoor temperature and solar radiation. Consequently, the duration of the heating season increases towards the north. In the northernmost city, Tromso, Norway (69.65° N), the heating season was determined to last 12 months. In the absence of solar energy, the transparent surface of the greenhouse is covered with thermal insulation to prevent heat loss. It has been shown that with the appropriate selection of this thermal covering, which is controlled based on the presence of instantaneous solar energy, up to 80% savings can be achieved from additional heating in southern cities. In the north, this rate can be increased up to a maximum of 70% by increasing the thermal covering thickness. Full article

At present, the soil of Chinese greenhouses is experiencing severe nitrogen input in the form of fertilizer, which will cause damage to the soil environment and restrict crop growth in the long run. The response of potential functions of microorganisms as drivers of nutrient cycling and material transformation to nitrogen enrichment has rarely been reported in northern vegetable planting systems. Therefore, we set up four cucumber pot experiments with different nitrogen addition rates (0, 258, 516, and 1032 kg N ha⁻¹ yr⁻¹) in the greenhouse. Bacterial and fungal communities were detected by 16S and ITS rRNA gene sequencing, and bacterial and fungal functional groups were predicted using the FAPROTAX and FUNGuild databases. The findings showed that nitrogen addition induced soil acidification (a decrease of 0.25–1.63 units) significantly reduced microbial diversity and changed the community composition of bacteria and fungi. The relative abundance of bacterial functional groups associated with the nitrogen cycle increased significantly when medium and high levels of nitrogen were added. Conversely, the bacterial functional groups involved in the carbon cycle exhibited the opposite pattern. In this study, NO₃⁻ and soil pH were the main factors affecting the soil microbial community and its functional groups. Our results highlight that hydrocarbon degradation and saprophytic fungi may play key roles in yield formation during cucumber cultivation in northern solar greenhouses. In general, adopting a fertilization strategy that ensures low-medium nitrogen availability can contribute to the sustainable progress of facility agriculture. Full article

The labor shortage and rising costs in the greenhouse industry have driven the development of automation, with the core of autonomous operations being positioning and navigation technology. However, precise positioning in complex greenhouse environments and narrow aisles poses challenges to localization technologies. This study proposes a multi-sensor fusion positioning and navigation robot based on ultra-wideband (UWB), an inertial measurement unit (IMU), odometry (ODOM), and a laser rangefinder (RF). The system introduces a confidence optimization algorithm based on weakening non-line-of-sight (NLOS) for UWB positioning, obtaining calibrated UWB positioning results, which are then used as a baseline to correct the positioning errors generated by the IMU and ODOM. The extended Kalman filter (EKF) algorithm is employed to fuse multi-sensor data. To validate the feasibility of the system, experiments were conducted in a Chinese solar greenhouse. The results show that the proposed NLOS confidence optimization algorithm significantly improves UWB positioning accuracy by 60.05%. At a speed of 0.1 m/s, the root mean square error (RMSE) for lateral deviation is 0.038 m and for course deviation is 4.030°. This study provides a new approach for greenhouse positioning and navigation technology, achieving precise positioning and navigation in complex commercial greenhouse environments and narrow aisles, thereby laying a foundation for the intelligent development of greenhouses. Full article

The food crisis has increased demand for agricultural resources due to various factors such as extreme weather, energy crises, and conflicts. A solar greenhouse enables counter-seasonal winter cultivation due to its thermal insulation, thus alleviating the food crisis. The root temperature is of critical importance, although the mechanism of soil thermal environment change remains uncertain. This paper presents a comprehensive study of the soil thermal environment of a solar greenhouse in Jinzhong City, Shanxi Province, employing a variety of analytical techniques, including theoretical, experimental, and numerical simulation, and deep learning modelling. The results of this study demonstrate the following: During the overwintering period, the thermal environment of the solar greenhouse floor was divided into a low-temperature zone, a constant-temperature zone, and a high-temperature zone; the distance between the low-temperature boundary and the southern foot was 2.6 m. The lowest temperature in the low-temperature zone was 11.06 °C and the highest was 19.05 °C. The floor in the low-temperature zone had to be heated; the lowest value of the constant-temperature zone was 18.29 °C, without heating. The minimum distance between the area of high temperature and the southern foot of the solar greenhouse was 8 m and the lowest temperature reading was 19.29 °C. The indoor soil temperature tended to stabilise at a depth of 45 cm, and the lowest temperature reading at a horizontal distance of 1400 mm from the south foot was 19.5 °C. The Fluent and LSTM models fitted well and the models can be used to help control soil temperature during overwintering in extreme climates. The research can provide theoretical and data support for the crop areas and the heating of pipelines in the solar greenhouse. Full article

The solar greenhouse is a significant agricultural facility in China. It enables the cultivation of crops during periods that do not coincide with the natural growing season, thus alleviating the pressure on the supply of fruits and vegetables during the winter months. The primary rationale behind the necessity of greenhouse cultivation lies in the fact that the temperature conditions conducive to optimal crop growth can be precisely replicated within this controlled environment. However, it is important to acknowledge that a distinct low-temperature area persists under the film during the overwintering period, with the precise delineation of its boundaries and distribution patterns remaining uncertain. In order to investigate the characteristics of the temperature distribution within the marginal region under the solar greenhouse film, experimental studies, CFD simulations, and LSTM prediction models were employed. The results of these studies indicate that, during the overwintering period, a low-temperature region was observed with approximately equal temperatures near the film membrane. The maximum horizontal distance from the south-side bottom corner was 6130 mm, while the minimum height from the ground was 600 mm. The lowest temperature in the low-temperature region was 4 °C, and the maximum observed temperature difference within the same period in different months was 1 °C. Additionally, a region of elevated temperatures was observed under the film. The lowest temperature in this region was 36.7 °C, and the highest temperature point was within the optimal range for crop growth. The CFD numerical simulation results were consistent with the actual observations, and the LSTM prediction model demonstrated high reliability. The findings of this study offer a theoretical foundation for the distribution of high and low temperatures in solar greenhouses. Furthermore, the developed prediction model provides the necessary buffer time for control, thus enhancing the efficiency of greenhouse cultivation. Full article

To address the structural concerns of a 12.0 m-span landing assembled single-tube frame (LASF) for Chinese solar greenhouses subjected to snow loads, the internal forces and deformations of LASF and its reinforced counterpart (RLASF) were numerically simulated to determine the ultimate bearing capacities (L_u) and the failure loads (L_f). During the simulations, steel tubes were modeled as beam188 elements and cables as link180 elements. The frame constraints and the connections were assumed to be fixed supports and rigid, respectively. The loads were determined according to the Chinese standard (GB51183-2016). Simulations revealed that the LASF and RLASF primarily withstand bending moments and are prone to strength failures under snow loads. Both exhibited lower L_u and L_f under non-uniform snow loads than under uniform snow loads. The results also indicated that crop loads could deteriorate the structural safety of the LASF and RLASF. L_u and L_f were found to be proportional to the section modulus of the tubes. The effects of wind loads and initial geometry imperfections on L_f of the LASF and RLASF can be neglected. Furthermore, the RLASF exhibited higher L_f compared to the LASF. Steel usage of the RLASF could be further reduced by replacing circular tubes with rectangular tubes, making the RLASF a feasible option for constructing Chinese solar greenhouses. Full article

In this study, a novel cambered snow removal device is designed to achieve automatic snow removal in large curved areas, such as the south roof of a Chinese solar greenhouse. The theory of structural parameters and shear force is ambiguous. People are not based on the greenhouse structure parameters for the selection of snow removal devices. Therefore, the quantitative relationship between the structure of the greenhouse span and the number of scissor arm-length knots is analysed, and the relationship between the material strength and application distance is determined. This study’s objectives are (1) to establish a theoretical model of scissor arm motion and (2) to analyse the force distribution of the scissor arm using multi-body dynamics. The results show that the scissor arm of a round-arch greenhouse has fewer sections but a larger arm length, whereas the scissor arm of a traditional solar greenhouse has more sections but a smaller arm length. Based on the shear force of the scissor structure, the optimised wall thickness reduces the force of the node by 17%. Full article

To enhance the utilization of solar energy in Chinese solar greenhouses (CSGs), a new method for optimizing the internal lighting environment of CSGs using reflective films is proposed. The influence of different positions and angles of reflecting film on solar radiation in greenhouses was studied, using the solar radiation on the inside surface of the CSG as an evaluation index. According to the findings, total solar radiation increased by 5.33% when the reflective film was positioned on the north roof at an angle of 0°. The light interception on the north wall decreased from 7.91% to 10.54% when the angle was raised from 15° to 25°. The crop canopy was not significantly affected by the reflective film’s various placements and angles, and the benefits of additional light were insufficient to compensate for the drawbacks of crop shading. This result provides a theoretical basis for the application of reflective films in relevant agricultural facilities. Reasonable installation of reflective film in the greenhouse can increase the light interception of plants inside the greenhouse and further increase the income of farmers. Full article

The use of renewable energy for food and vegetable production is a potential sustainable method to reduce fossil energy consumption. Chinese solar greenhouses (CSGs) are horticultural facility buildings in the northern hemisphere that use solar energy to produce off-season vegetables in winter. The north wall heat storage and release capacity of CSG has a significant impact on the indoor thermal–humidity environment. However, common traditional solar greenhouses commonly have problems such as insufficient heat storage and release, thick temperature stability zones inside the walls, and inability to dynamically regulate the entire greenhouse environment. Therefore, a novel active–passive heat storage wall system (APHSWS) incorporating phase change materials has been developed to promote the thermal performance of the CSG and its internal temperature of the thermal storage wall in this paper. Through experimental and simulation methods, the heat storage and release of the APHSWS and its impact on the greenhouse environment are investigated. The findings indicate that the APHSWS has increased the wall heat storage and release capacity, compared to the ordinary greenhouse without the APHSWS, in three typical weather conditions in winter (i.e., sunny, overcast, and cloudy); the average temperature of greenhouse with the APHSWS has increased in indoor air temperature, wall surface temperature, and soil surface temperatures of 1.58–6.06 °C, 2.71–6.58 °C, 0.91–6.39 °C, respectively; and during the experiment, the greenhouse with the APHSWS has a monthly average daily effective accumulated temperature of 1.39 times, 1.18 times, 0.60 times, and 0.20 times that of the ordinary greenhouse without the APHSWS from December to March of the next year, respectively. Under typical sunny conditions, the greenhouse wall heat storage capacity increased by 1.59–2.44 MJ/m² and the heat release capacity increased by 0.97–1.17 MJ/m². At the direction of wall thickness, the temperature at each point inside the wall with the APHSWS is always higher than that of ordinary wall without the APHSWS. In addition, the operating cost of the APHSWS in winter is analyzed. Full article

Plant height and leaf area index (LAI) are crucial growth indicators that reflect the growth status of tomatoes in greenhouses, enabling accurate determinations to effectively estimate crop transpiration and formulate irrigation strategies for reducing agricultural water waste. There is a need for the increased application of related models to simulate tomato growth indices in the traditional greenhouse production in China. This study proposes a nondestructive, real-time monitoring and simulation device for measuring tomato plant height and leaf area index. The weight of aboveground tomatoes was obtained by suspending tomato plants on dynamometers, while the total weight of stem and leaf organs was determined using a distribution coefficient simulation model. The R² value between the measurements from the electronic scale and those from the aboveground fresh weight device for tomatoes was 0.937, with an RMSE value of 0.05 kg. The monitoring device did not affect the average tomato growth during operation. The device will not affect the growth of tomatoes during monitoring. A multiple linear regression was used to compare the measured and simulated values of the plant height and leaf area index of various types of greenhouse tomatoes cultivated in different greenhouse types. The average R² value for simulating plant height was 0.817 with an RMSE of 10.81 cm. The average R² value for the leaf area index was 0.854, with an RMSE of 0.55 m²·m⁻². The simulated values for plant height and leaf area index closely matched the measured values, indicating that the model has high accuracy and applicability in traditional Chinese greenhouses (solar greenhouses and insulated plastic greenhouses). However, further optimization is required for commercially produced, continuous plastic greenhouses equipped with greenhouse environmental control equipment. Full article