The race for automation has reached farms and agricultural fields. Many of these facilities use the Internet of Things technologies to automate processes and increase productivity. Besides, Machine Learning and Deep Learning allow performing continuous decision making based on data analysis. In this work, we fill a gap in the literature and present a novel architecture based on IoT and Machine Learning / Deep Learning technologies for the continuous assessment of agricultural crop quality. This architecture is divided into three layers that work together to gather, process, and analyze data from different sources to evaluate crop quality. In the experiments, the proposed approach based on data aggregation from different sources reaches a lower percentage error than considering only one source. In particular, the percentage error achieved by our approach in the test dataset was 6.59, while the percentage error achieved exclusively using data from sensors was 6.71.

The present paper addresses the development and use of a new potentiometric electronic tongue for both qualitative and quantitative characterization of natural mineral waters. The electronic tongue is particularly related to the conductivity and ion content of/in the water sample. The analytical system is based on six ion-selective electrodes whose membranes are formulated to provide either cationic or anionic response and considering plasticizers with different dielectric constants (bis(2-ethylhexyl) sebacate, 2-nitrophenyl octyl ether or tricresylphosphate), while keeping the polymeric matrix, i.e., poly(vinyl chloride). Notably, the absence of any ionophore in the membrane provides a general response profile, i.e., no selectivity toward any special ion, which is convenient for the realization of an effective electronic tongue. The dynamic response of the tongue toward water samples of different chemical compositions and geographical locations has been obtained. At the optimized experimental conditions, the tongue presents acceptable repeatability and reproducibility (absence of hysteresis). The principal component analysis of the final potential values observed with the six electrodes allows for the differentiation and classification of the samples according to their conductivity, which is somehow related to the mineralization. Moreover, quantitative determination of the six main ions in the water samples (i.e., chloride, nitrate, hydrogen carbonate, sulfate, sodium, calcium, and magnesium) is possible by means of a simple linear calibration (and cross-validation) model.

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A study of the transient potential signals obtained with a cation-selective electrode based on an ion-exchanger was carried out for solutions of the following individual cations at different concentrations: H+, Li+, Na+, K+, Rb+, Mg2+, Ca2+, choline (Ch+), acetylcholine (AcCh+), and procaine (Pr+). Three different general types of transient signals were distinguished depending on the value of the selectivity coefficient of the corresponding ion. A principal component analysis (PCA) was performed on the signals, finding that the qualitative identification of the corresponding ion from the scores of two principal components is possible. The study was extended to the transient signals of solutions containing an analyte in the presence of an interfering ion. The PCA of the corresponding signal allows for the detection of the presence of interfering ions, thus avoiding biased results in the determination of the analyte. Moreover, the two principal components of the transient signals obtained for each of the ions at different concentrations allow for the construction of calibration graphs for the quantitative determination of the corresponding ion. All the transient signals obtained experimentally in this work can be reconstructed accurately from principal components and their corresponding scores.

El empleo de actividades que permitan mantener activo el interés al alumnado en distintas materias es un instrumento fundamental para mejorar el aprendizaje y ampliar conocimientos en todas las áreas educativas. Modding, derivado de la jerga inglesa modify (modificar), es una técnica consistente en cambiar varios aspectos de equipos informáticos. Por un lado, hacer más atractiva la parte física del ordenador y, por otro, mejorar las prestaciones de dichos equipos. Mediante la experiencia Modding presentada en este trabajo, a los estudiantes se les plantea un concurso dentro de las áreas denominadas STEM (Science, Technology, Engineering and Mathematics) con el propósito de mejorar, tanto visualmente como en términos de rendimiento, equipos informáticos, muchos de ellos en desuso, pero todavía funcionales. El concurso está abierto a todos los niveles del centro educativo, de forma interdisciplinar, ya que son varios los departamentos y asignaturas que pueden participar por encontrarse recogidos parte de los contenidos trabajados en la actividad dentro del currículo docente. Además, dicha interdisciplinariedad se amplía también a niveles de educación superior, haciendo partícipe del concurso al Departamento de Informática de la Universitat de València. En concreto, este último será el encargado de presentar a los participantes las  distintas mejoras a nivel hardware y software que se pueden incluir para mejorar el rendimiento de equipos, como por ejemplo la refrigeración líquida, o la importancia de la ventilación en sus prototipos.