Combined SPRi Sensor for Simultaneous Detection of Nitrate and Ammonium in Wastewater

Progress in Plasmonic Sensors as Monitoring Tools for Aquaculture Quality Control

Aquaculture is an expanding economic sector that nourishes the world's growing population due to its nutritional significance over the years as a source of high-quality proteins. However, it has faced severe challenges due to significant cases of environmental pollution, pathogen outbreaks, and the lack of traceability that guarantees the quality assurance of its products. Such context has prompted many researchers to work on the development of novel, affordable, and reliable technologies, many based on nanophotonic sensing methodologies. These emerging technologies, such as surface plasmon resonance (SPR), localised SPR (LSPR), and fibre-optic SPR (FO-SPR) systems, overcome many of the drawbacks of conventional analytical tools in terms of portability, reagent and solvent use, and the simplicity of sample pre-treatments, which would benefit a more sustainable and profitable aquaculture. To highlight the current progress made in these technologies that would allow them to be transferred for implementation in the field, along with the lag with respect to the most cutting-edge plasmonic sensing, this review provides a variety of information on recent advances in these emerging methodologies that can be used to comprehensively monitor the various operations involving the different commercial stages of farmed aquaculture. For example, to detect environmental hazards, track fish health through biochemical indicators, and monitor disease and biosecurity of fish meat products. Furthermore, it highlights the critical issues associated with these technologies, how to integrate them into farming facilities, and the challenges and prospects of developing plasmonic-based sensors for aquaculture.

IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

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.