RESUMEN

Urban design is currently promoting the inclusion of plants in buildings. However, plants emit biogenic volatile organic compounds (BVOCs), which alone or in combination with other airborne molecules such as CO2, may result in a general increase in tropospheric pollution. Many studies have documented the effects of biotic and abiotic factors on plant BVOC responses, but few have assessed the contribution of typical CO2 levels found in indoor work and meeting spaces. To answer this question, we monitored CO2 and constitutive (MT-limonene) and induced (LOX-cis-3-hexenal) BVOC emissions of a fully developed tomato crop grown hydroponically inside an integrated rooftop greenhouse (i-RTG) in a Mediterranean climate. Two distinctive CO2 assays were performed at the level of the i-RTG by supplying or not CO2. The impact of CO2 on plant physiological emittance was then assessed, and the resulting BVOC rates were compared with reference to EU-LCI values. MT-limonene was ubiquitous among the assays and the most abundant, while LOX-cis-3-hexenal was detected only under controlled CO2 management. The highest levels detected were below the indicated LCIs and were approximately tenfold lower than the corresponding LCI for MT-limonene (50.88 vs. 5000 µg m-3) and eightfold (6.63 µg m-3) higher than the constitutive emission level for LOX-cis-3-hexenal. Over extended sampling (10 min) findings revealed a general emission decrease and significantly different CO2 concentration between the assays. Despite similar decreasing rates of predicted net photosynthesis (Pn) and stomatal conductance (gs) their correlation with decreasing CO2 under uncontrolled condition indirectly suggested a negative CO2 impact on plant emission activity. Conversely, increasing CO2 under the controlled assay showed a positive correlation with induced emissions but not with constitutive ones. Because of significantly higher levels of relative humidity registered under the uncontrolled condition, this factor was considered to affect more than CO2 the emission response and even its collection. This hypothesis was supported by literature findings and attributed to a common issue related with the sampling in static enclosure. Hence, we suggested a careful monitoring of the sampling conditions or further improvements to avoid bias and underestimation of actual emissions. Based on the main outcomes, we observed no evidence of a hazardous effect of registered CO2 rates on the BVOC emissions of tomato plant. Furthermore, because of the low BVOC levels measured in the i-RTG, we assumed as safe the recirculation of this air along building's indoor environments.

RESUMEN

The rapid change in population, environment, and climate is accompanied by the food crisis. As a new type of farming, indoor agriculture opens the possibility of addressing this crisis in the future. In this study, a phosphor-converted light-emitting diode (pc-LED), as energy-saving lighting for indoor agriculture, was used to evaluate the response and effect on the growth of Lactuca sativa. Red phosphors, SrLiAl3N4:Eu2+ (SLA) and CaAlSiN3:Eu2+ (CASN), were characterized and analyzed with crystal structure, morphology, and optical properties. Eu2+-doped phosphors provided the red emission of around 650 nm which is highly matched with the absorption of chlorophyll. Under the same luminescence intensity, broader emission of CASN pc-LED demonstrated a 100% increase of photosynthetically active photon flux density and 130% promotion of plant weight than the SLA pc-LED, which reflected the positive result of the carbon fixation. The chlorophyll and nitrate responses have also revealed the effect of broader red light on indoor agriculture.

RESUMEN

Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop monitoring, detecting biotic and abiotic stresses, irrigation, microclimate prediction, energy efficient controls, and crop growth prediction. However, no review study assess DL's state of the art to solve diverse problems in CEA. To fill this gap, we systematically reviewed DL methods applied to CEA. The review framework was established by following a series of inclusion and exclusion criteria. After extensive screening, we reviewed a total of 72 studies to extract the useful information. The key contributions of this article are the following: an overview of DL applications in different CEA facilities, including greenhouse, plant factory, and vertical farm, is presented. We found that majority of the studies are focused on DL applications in greenhouses (82%), with the primary application as yield estimation (31%) and growth monitoring (21%). We also analyzed commonly used DL models, evaluation parameters, and optimizers in CEA production. From the analysis, we found that convolutional neural network (CNN) is the most widely used DL model (79%), Adaptive Moment Estimation (Adam) is the widely used optimizer (53%), and accuracy is the widely used evaluation parameter (21%). Interestingly, all studies focused on DL for the microclimate of CEA used RMSE as a model evaluation parameter. In the end, we also discussed the current challenges and future research directions in this domain.

Asunto(s)

Aprendizaje Profundo , Redes Neurales de la Computación , Agricultura , Ambiente Controlado

RESUMEN

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.