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.

Great advancements in technologies such as big data analytics, robots, remote sensing, the Internet of Things, decision support systems and artificial intelligence have transformed the agricultural sector. In the greenhouse sector, these technologies help farmers increase their profits and crop yields while minimizing the production costs, produce in a more environmentally friendly way and mitigate the risks caused by climate change. In greenhouse farming, especially in the Mediterranean region, a lack of knowledge and qualified personnel able to uptake new knowledge, the small size of farms, etc., make it difficult to implement new technologies. Although it is necessary to demonstrate the advantages of innovations related to sustainable agriculture, there is a little opportunity for specific training on greenhouse production in cutting-edge technologies. To gain insight into this problem, questionnaires for greenhouse farmers and intermediaries were developed in multiple choice format and filled in by the stakeholders. A statistical analysis was performed, and the results are presented in graphical form. In most cases, the findings confirmed that producers who run small farms, in most cases, have a lack of knowledge, especially on how to manage climate control systems or fertigation systems. The majority of farmers were elderly with a low level of education, which makes it difficult to be aware of the training issues, due to distrust and a lack of innovation culture. Therefore, their strategy was usually survival with cost control. However, young graduates have been recently returning to agriculture, and they are open to training activities and innovation. The most desirable training offer should be related to sustainable agriculture and precision agriculture technologies.

The control of large greenhouse installations, especially those with hydroponics crops, is based on the analysis and use of data recorded by many sensors. At the same time, the size of such installations does not allow for their effective terrestrial surveillance, to detect problems promptly. In recent years, there has been an interest in the development of autonomous agbots equipped with agricultural sensors. Several ground-based AGV (automated guided vehicles) and UAV (unmanned aerial vehicles) systems have been developed for use in open-air plots. A key feature of all these innovative systems is spectroscopy, the development of which has been assisted by the surveillance capabilities and speed of modern-day UAVs (drones). However, there is a lag in the use of spectroscopy inside greenhouses since UAVs do not move freely indoors. In this paper, we propose as a solution a three-device (3DS) system.

Background: Detecting impaired naming capacity is valuable in diagnosing neurocognitive disorders (ND). A. clinical practice- oriented overview of naming tests validated in ND is not available yet. Here, features of naming tests with validated utility in ND which are open access or available for purchase are succinctly presented and compared. Methods: Searches were carried out across Pubmed, Medline and Google Scholar. Additional studies were identified by searching reference lists. Only peer-reviewed journal articles were eligible. A narrative- and tabullar synthesis was used to summarize different aspects of the naming assessment instruments used in patients with ND such as stimuli type, administration time, assessment parameters and accessibility. Based on computational word frequency calculations, the tests were compared in terms of the average frequency of their linguistic content. Results: Twelve naming tests, relying either on visual or auditory stimuli have been validated in ND. Their content and administration time vary between three and 60 items and one and 20 minutes, respectively. The average frequency of the words of each considered test was two or lower, pointing to low frequency of most items. In all but one test, scoring systems are exclusively based on correctly named items. Seven instruments are open access and four are available in more than one language. Conclusions: Gaining insights into naming tests’ characteristics may catalyze the wide incorporation of those with short administration time but high diagnostic accuracy into the diagnostic workup of ND at primary healthcare and of extensive, visual or auditory ones into the diagnostic endeavors of memory clinics, as well as of secondary and tertiary brain healthcare settings.

Integration of photovoltaic modules into greenhouse roofs is a novel and intriguing method. The cost of products grown in greenhouses is particularly high because of their high energy consumption for heating and cooling, and at the same time the increase in demand for available land, increasing its cost and creating spatial issues, the integration of photovoltaics on the roof of greenhouses is a highly viable solution. Simultaneously, the use of solar radiation is critical to maintain optimal crop development, while also being a renewable energy source. However, photovoltaics reduce the incoming solar radiation in the greenhouse, due to their shade. Shading can be either beneficial for the crops or not, depending on the crop type, thus it is vital to find the shading caused by photovoltaics both temporally and spatially. In this study, a model calculating the shading in a greenhouse due to roof-integrated photovoltaics is developed, based on the Sun position, the geometry of both the greenhouse and of the roof-integrated photovoltaics and their position on the greenhouse roof. Calculating the coefficient of variation of radiation data, for the shaded and unshaded areas using the proposed algorithm, it was found the coefficient of variation for the shaded areas is lower than that for the unshaded areas for a least 76% of the time. Also, the radiation values under the shaded area are more uniform. The proposed model is a tool for PV designers, operators, and owners, in order to optimize the potential of their solar panel installations.