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Several years have passed since the linear economy model proved unsustainable, leading to the transition toward the circular economy (CE) model. Significant amounts of agricultural residues and waste from livestock farming units remain unutilized in fields. The anaerobic digestion (AD) method addresses this issue by generating energy in the form of thermal (TE) and electrical energy (EE). This article examines greenhouse heating using thermal energy from a biogas plant. For this purpose, a thermal load model is developed and applied in two regions, northern (Florina) and central Greece (Trikala), to assess the greenhouse’s energy requirements in areas with differing characteristics, especially during the winter months. Additionally, the economic benefits of a biogas plant from selling electricity to the grid are analyzed. Thermal energy constitutes 59.7% of the system’s total energy output. On average, the generated electrical energy amounts to 518 MW h per month, while thermal energy reaches 770 MW h per month. The biogas plant’s daily electricity consumption ranges from 1564 kW h to 2173 kW h, depending on its needs. Ambient temperatures vary between 0 °C and 37 °C, significantly influencing the greenhouse heating system’s efficiency. The biogas plant also demonstrates financial profitability, earning 504,549 € annually from the sale of surplus electricity. Furthermore, the article explores greenhouse crops in the broader Thessaly region, where tomato cultivation seems to be dominant. Greenhouse heating requirements depend on crop type, location, weather conditions, sunlight exposure, and heat loss based on covering materials. Meanwhile, the thermal energy output that can heat a given greenhouse area is directly proportional to the biogas plant’s capacity.

Radiation is a crucial factor in the field of agrivoltaics in greenhouses. Depending on the type of photovoltaics integrated into greenhouses, the effect on radiation varies through the phenomenon of shading. Shading in greenhouses can be either beneficial or detrimental, making its analysis imperative. In this study, the improvements and modifications made in an algorithm capable of calculating the shading from photovoltaic units installed in greenhouses are presented. A key modification to the algorithm is the calculation of shading from semi-transparent photovoltaic modules, in contrast to its original form, in which photovoltaic modules were considered opaque. The algorithm was validated using radiation data from pyranometers within a greenhouse. The coefficients used were Pearson's r correlation coefficient and the Coefficient of Variation. The correlation coefficients for times without shading effect approached the values for the case without photovoltaics installed on the roof. Simultaneously, based on the coefficients of variation, the uniformity of radiation within the shade was validated, thereby confirming its existence. Finally, the effect of semi-transparent photovoltaic units on Global Horizontal Irradiance and Photosynthetically Active Radiation was studied, with the reduction for the former approaching 52 % and about 60 % for the latter. These changes in radiation can be either beneficial or not, depending on the type of crop and the needs of the greenhouse, such as cooling.

In recent years, the global demand for medicinal plants has been rising steadily. This study focuses on cultivating Aloe vera within a large-scale, pilot smart greenhouse installed at the University of Patras, Greece. The integration of advanced Internet of Things (IoT) technologies, sensors, and the KYTION cablebot robotic device plays a crucial role in monitoring the cultivation process and deriving valuable insights. The greenhouse structure presented in this study not only recognizes these challenges but actively addresses them through state-of-the-art IoT technology-based measurements. Preliminary quantitative results indicate that cultivating A. vera in greenhouses significantly enhances blooming and production compared to open-field cultivation. This promising approach addresses the growing demand for A. vera specifically and medicinal plants in general. Additionally, it maximizes the use of available arable land, labor, water, and energy, ensuring stable production under unstable and variable climatic conditions. The study aims to control and automate the microclimate, optimizing conditions for A. vera growth within a controlled digital environment. By offering insights into the intersection of smart agricultural practices, technology-driven measurement and automation, and ad-hoc crop-specific cultivation strategies, this research provides a promising pathway for sustainably enhancing A. vera cultivation in controlled environments.

This research aims to address the critical challenges caused by global population increase, climate change, and urbanization on food production by means of a Fuzzy Logic controller in a small-scale greenhouse and improving agricultural efficiency and sustainability.The growing global population has stressed the significance of implementing efficient food production techniques. Furthermore, the impact of climate change and extreme weather events complicates the production process. The goal of this study is to combine an innovative small greenhouse design with a fuzzy logic-based control system in order to develop an IoT-enabled self-regulating environment for autonomous plant growing.Continuous advances in technology have been focusing on automating greenhouses to provide self-regulating microclimates and optimal developmental conditions. Current trends focus on automating greenhouses to provide self-regulating microclimates. Looking ahead, the strategy entails combining automated approaches with precisely planned structures via IoT.This study's key findings include the successful integration of a fuzzy logic control system with greenhouse infrastructure, allowing for real-time monitoring and adjustment of environmental parameters, as well as the development of a pilot IoT ecosystem demonstrating the feasibility of self-regulating microclimates for improved plant growth.

The intensity of ultraviolet (UV) radiation affects the yield and quality of red lettuce. The current study aimed to develop a UV management system in a greenhouse to achieve high yield and quality in red lettuce production. The study consisted of two experiments. In the first experiment, the effects of the different UV transparencies of the plastic materials covering the greenhouse on plant growth and the concentration of antioxidants in red lettuce were studied. For this purpose, two greenhouses were covered with polyethene of different transparencies to UV radiation. One greenhouse was covered with a common type of polyethene transparent in a large spectrum of UV radiation (UV-open), while the second greenhouse was covered with polyethene untransparent to ultraviolet radiation (UV-block). The plants were grown in a deep flotation hydroponic system. At the end of the cultivation, plant growth measurements, leaf colour measurements, and the determination of antioxidant components’ concentration were carried out. Red lettuce plants harvested 42 days after planting had an average head weight 42% greater in the UV-block greenhouse compared to plants grown in the UV-open greenhouse. However, the red leaf colour of plants in the UV-block greenhouse lagged significantly compared to that in the UV-open greenhouse. Moreover, the total phenolic content, the total flavonoid content, and the antioxidant capacity of the lettuce leaves in the UV-block greenhouse were significantly lower compared to the corresponding values of the plants in the UV-open greenhouse. During the second experiment, a new cultivation system of red lettuce, which combined a UV-block polyethene film as a greenhouse cover and a pre-harvested supplemental UV light, was tested. For this purpose, various doses of supplemental UV lighting were tested in the UV-block greenhouse for ten days prior to harvest. From these tests, it emerged that applying supplemental UV lighting with a dose of 425 kJ m−2 d−1 for ten days before harvest produces red lettuces of the same quality as those produced in a UV-open greenhouse. This technique of growing red lettuce increases its yield by 30% without a negative effect on the quality of the product.

Food production and energy consumption are two important factors when assessing greenhouse systems. The first must respond, both quantitatively and qualitatively, to the needs of the population, whereas the latter must be kept as low as possible. As a result, to properly control these two essential aspects, the appropriate greenhouse environment should be maintained using a computational decision support system (DSS), which will be especially adaptable to changes in the characteristics of the external environment. A multilayer perceptron neural network (MLP-NN) was designed to model the internal temperature and relative humidity of an agricultural greenhouse. The specific NN uses Levenberg–Marquardt backpropagation as a training algorithm; the input variables are the external temperature and relative humidity, wind speed, and solar irradiance, as well as the internal temperature and relative humidity, up to three timesteps before the modeled timestep. The maximum errors of the modeled temperature and relative humidity are 0.877 K and 2.838%, respectively, whereas the coefficients of determination are 0.999 for both parameters. A model with a low maximum error in predictions will enable a DSS to provide the appropriate commands to the greenhouse actuators to maintain the internal conditions at the desired levels for cultivation with the minimum possible energy consumption.