Hydroponics refers to a modern set of agricultural techniques that do not require the use of natural soil for plant germination and development. These types of crops use artificial irrigation systems that, together with fuzzy control methods, allow plants to be provided with the exact amount of nutrients for optimal growth. The diffuse control begins with the sensorization of the agricultural variables that intervene in the hydroponic ecosystem, such as the environmental temperature, electrical conductivity of the nutrient solution and the temperature, humidity, and pH of the substrate. Based on this knowledge, these variables can be controlled to be within the ranges required for optimal plant growth, reducing the risk of a negative impact on the crop. This research takes, as a case study, the application of fuzzy control methods to hydroponic strawberry crops (Fragaria vesca). It is shown that, under this scheme, a greater foliage of the plants and a larger size of the fruits are obtained in comparison with natural cultivation systems in which irrigation and fertilization are carried out by default, without considering the alterations in the aforementioned variables. It is concluded that the combination of modern agricultural techniques such as hydroponics and diffuse control allow us to improve the quality of the crops and the optimization of the required resources.

This work shows the use of Big Data and Data Mining techniques on vegetable crops data from a greenhouse by implementing the first version of a software tool, so called GreenFarm-DM. Such a tool is aimed at analyzing the factors that influence the growth of the crops, and determine a predictive model of soil moisture. Within a greenhouse, the variables that affect crop growth are: relative humidity, soil moisture, ambient temperature, and levels of illumination and CO2. These parameters are essential for photosynthesis, i.e. during processes where plants acquire the most nutrients, and therefore, if performing a good control on these parameters, plants might grow healthier and produce better fruits. The process of analysis of such factors in a data mining context requires designing an analysis system and establishing an objective variable to be predicted by the system. In this case, in order to optimize water resource expenditure, soil moisture has been chosen as the target variable. The proposed analysis system is developed in a user interface implemented in Java and NetBeans IDE 8.2, and consists mainly of two stages. One of them is the classification through algorithm C4.5 (chosen for the first trial), which uses a decision tree based on the data entropy, and allows to visualize the results graphically. The second main stage is the prediction, in which, from the classification results obtained in the previous stage, the target variable is predicted from information of a new set of data. In other words, the interface builds a predictive model to determine the behavior of soil moisture.

The present wireless sensor network system shows a data analysis approach within greenhouses in short cycle crops. This research, on the one hand, is carried out to reduce water consumption and improve the product by predicting the right moment of the irrigation cycle through the evapotranspiration criterion. On the other hand, an efficient electronic system is designed under the electronic standard. To define the best model to define the next irrigation in the crops in base to ground humidity, the algorithms are compared for continuous and discontinuous multivariate regressions. The results are evaluated with different criteria of prediction errors. As a result, the linear regression with Support Vector Machine model is chosen for counting an average deviation error of 7.89% and an error variability of 4.48%. In addition, water consumption is reduced by 20%, achieving better quality products.

En el campo de la Inteligencia de Negocios, la tendencia se está orientando hacia la utilización de herramientas de BI modernas basadas en memoria y con capacidades de lógica asociativa de consultas; que además permitan dar soporte a la toma de decisiones, presentando la información de una manera sencilla e intuitiva y permitiendo al usuario poseer el conocimiento que necesita oportunamente. El objetivo del presente estudio fue identificar las principales características de las tecnologías de BI en memoria y de lógica asociativa de consultas que utilizan las plataformas de BI modernas. La metodología se centró en el tipo de investigación documental, la técnica empleada fue el aná- lisis de contenido a través de la revisión de fuentes bibliográficas. Conclusión: El análisis de información permitió identificar que las principales características de las tecnologías de BI en memoria son: (a) velocidad de análisis, (b) visualización interactiva, (c) procesamiento de memoria, (d) autoservicio y (e) flexibilidad; las principales características de la lógica asociativa son (a) exploración de datos asociativa, (b) cálculo dinámico para todas las visualizaciones (c) preservación de las asociaciones entre los campos que se analizan. Estas características permiten que una solución de inteligencia de negocios sea ágil, flexible, fácil de utilizar y que los usuarios sean menos dependientes de TI.

Deep neural networks (DNNs) have emerged as an effective technique for modulation/system recognition but rely heavily on representative datasets. This paper introduces the "UPC-LPWAN-1" dataset, a comprehensive collection of 40 Sub-GHz LPWAN transmission modes acquired using real hardware. Publicly available to the scientific community, this dataset includes raw and pre-processed samples across different Signal-to-Noise Ratios (SNRs) and features multi-carrier modulations, which are typically underrepresented in existing datasets. The variability in studies using different neural network architectures and small, unrepresentative datasets complicates research comparisons. To address this, this paper compares seven proposed architectures using UPC-LPWAN-1, providing a standardized evaluation. To further enhance accuracy, we propose four new convolutional neural network (CNN) architectures adapted to four forms of signal representation. Our results demonstrate that while some existing models perform well under high SNR conditions, their performance degrades significantly in low SNR environments. The proposed spectrogram-based CNN consistently outperforms other models, achieving a classification accuracy of 99.71% at SNR=0 dB, above 90% at SNR=-10 dB, and above 70% at SNR=-15 dB, while still being able to differentiate between systems.