Search Results (5)

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

Most greenhouses in the Canadian Prairies shut down during the coldest months (November to February) because of the hefty heating cost. Chinese mono-slope solar greenhouses do not primarily rely on supplemental heating; instead, they mostly rely on solar energy to maintain the required indoor temperature in winter. This study focuses on improving an existing thermal model, entitled RGWSRHJ, for Chinese-style solar greenhouses (CSGs) to increase the robustness of the model for simulating the thermal environment of the CSGs located outside of China. The modified model, entitled SOGREEN, was validated using the field data collected from a CSG in Manitoba, Canada. The results indicate that the average prediction error for indoor and relative humidity is 1.9 °C and 7.0%, and the rRMSE value is 3.3% and 11.5%, respectively. The average error for predicting the north wall and ground surface temperature is 4.2 °C and 2.3 °C, respectively. The study also conducted a case study to analyze the thermal performance of a conceptual CSG in Saskatoon, Canada. The energy analysis indicates the heating requirement of the greenhouse highly depends on the availability of solar radiation. Besides winter, the heating requirement is relatively low in March to maintain 18 °C indoor temperature when the average outdoor temperature was below –4 °C, and negligible during May–August. The results indicate that vegetable production in CSGs could save about 55% on annual heating than traditional greenhouses. Hence, CSGs could be an energy-efficient solution for ensuring food security for northern communities in Canada and other cold regions. Full article

Yield prediction for tomatoes in greenhouses is an important basis for making production plans, and yield prediction accuracy directly affects economic benefits. To improve the prediction accuracy of tomato yield in Chinese-style solar greenhouses (CSGs), a wavelet neural network (WNN) model optimized by a genetic algorithm (GA-WNN) is applied. Eight variables are selected as input parameters and the tomato yield is the prediction output. The GA is adopted to optimize the initial weights, thresholds, and translation factors of the WNN. The experiment results show that the mean relative errors (MREs) of the GA-WNN model, WNN model, and backpropagation (BP) neural network model are 0.0067, 0.0104, and 0.0242, respectively. The results root mean square errors (RMSEs) are 1.725, 2.520, and 5.548, respectively. The EC values are 0.9960, 0.9935, and 0.9868, respectively. Therefore, the GA-WNN model has a higher prediction precision and a better fitting ability compared with the BP and the WNN prediction models. The research of this paper is useful from both theoretical and technical perspectives for quantitative tomato yield prediction in the CSGs. Full article

The area covered by Chinese-style solar greenhouses (CSGs) has been increasing rapidly. However, only a few pyranometers, which are fundamental for solar radiation sensing, have been installed inside CSGs. The lack of solar radiation sensing will bring negative effects in greenhouse cultivation such as over irrigation or under irrigation, and unnecessary power consumption. We aim to provide accurate and low-cost solar radiation estimation methods that are urgently needed. In this paper, a method of estimation of solar radiation inside CSGs based on a least mean squares (LMS) filter is proposed. The water required for tomato growth was also calculated based on the estimated solar radiation. Then, we compared the accuracy of this method to methods based on knowledge of astronomy and geometry for both solar radiation estimation and tomato water requirement. The results showed that the fitting function of estimation data based on the LMS filter and data collected from sensors inside the greenhouse was y = 0.7634x + 50.58, with the evaluation parameters of R² = 0.8384, rRMSE = 23.1%, RMSE = 37.6 Wm⁻², and MAE = 25.4 Wm⁻². The fitting function of the water requirement calculated according to the proposed method and data collected from sensors inside the greenhouse was y = 0.8550x + 99.10 with the evaluation parameters of R² = 0.9123, rRMSE = 8.8%, RMSE = 40.4 mL plant⁻¹, and MAE = 31.5 mL plant⁻¹. The results also indicate that this method is more effective. Additionally, its accuracy decreases as cloud cover increases. The performance is due to the LMS filter’s low pass characteristic that smooth the fluctuations. Furthermore, the LMS filter can be easily implemented on low cost processors. Therefore, the adoption of the proposed method is useful to improve the solar radiation sensing in CSGs with more accuracy and less expense. Full article

Crop production in open fields is increasingly limited by weather extremes and water shortages, in addition to pests and soil-borne diseases. In order to increase crop yield, quality, and productivity, controlled environment agriculture (CEA) can play an important role as an alternative and supplemental production system to conventional open field production. CEA is any agricultural technology that enables growers to manipulate the growing environment for improved yield and quality. CEA production systems include high tunnels, greenhouses, and indoor vertical farming, as well as hydroponics and aquaponics. Currently, ‘low-tech’ CEA techniques such as high tunnels (plastic greenhouses with minimum or no cooling and heating) are primarily utilized in developing countries where labor costs are relatively low, and China has by far the largest area covered by high tunnels or ‘Chinese-style’ solar greenhouses. The most control-intensive ‘high-tech’ CEA approach, namely indoor vertical farming, has gained tremendous attention in the past decade by researchers and entrepreneurs around the world, owing to advancements in lighting technology, including use of light emitting diodes (LEDs), and increasing urbanization with new market opportunities. This special issue covers some of the CEA topics such as LED lighting, substrate, and hydroponics. Full article