Measurement of the broadband complex permittivity of soils in the frequency domain with a low-cost vector network analyzer and an open-ended coaxial probe

The performance of a handheld Vector Network Analyzer (VNA), the nanoVNA, a low-cost, open-source instrument, was evaluated. The instrument measures the complex permittivity of dielectric media from 1-port reflection parameters in the 1 – 900 MHz bandwidth. We manufactured an open-ended coaxial probe using a SMA-N coaxial adapter to perform dielectric measurements. The accuracy of the nanoVNA was comparable to that of a commercial VNA between 1 and 500 MHz according to tests in reference organic liquids, while a lack of stability was found beyond 700 MHz. The self-manufactured open-ended coaxial probe was subjected to a Finite Element Method (FEM) analysis and its electromagnetic (EM) field penetration depth was determined to be 1.5 mm at 100 MHz, being reduced to 1.3 at 900 MHz and thus demonstrating a frequency-dependent support volume. The broadband complex permittivity of three mineral soils of varied textures was obtained for a range of bulk densities and water contents from dry to water-saturated conditions. The dielectric response of the soils approximated the well-known Topp et al. (1980) equation at high frequencies. At lower frequency however, higher permittivities were exhibited due to dielectric dispersion, which emphasizes the importance of EM-based soil moisture sensor operating frequency when considering sensor calibration or comparing the response of different sensors

Preferential Occupancy of R2 Retroelements on the B Chromosomes of the Grasshopper Eyprepocnemis plorans

R2 non-LTR retrotransposons exclusively insert into the 28S rRNA genes of their host, and are expressed by co-transcription with the rDNA unit. The grasshopper Eyprepocnemis plorans contains transcribed rDNA clusters on most of its A chromosomes, as well as non-transcribed rDNA clusters on the parasitic B chromosomes found in many populations. Here the structure of the E. plorans R2 element, its abundance relative to the number of rDNA units and its retrotransposition activity were determined. Animals screened from five populations contained on average over 12,000 rDNA units on their A chromosomes, but surprisingly only about 100 R2 elements. Monitoring the patterns of R2 insertions in individuals from these populations revealed only low levels of retrotransposition. The low rates of R2 insertion observed in E. plorans differ from the high levels of R2 insertion previously observed in insect species that have many fewer rDNA units. It is proposed that high levels of R2 are strongly selected against in E. plorans, because the rDNA transcription machinery in this species is unable to differentiate between R2-inserted and uninserted units. The B chromosomes of E. plorans contain an additional 7,000 to 15,000 rDNA units, but in contrast to the A chromosomes, from 150 to over 1,500 R2 elements. The higher concentration of R2 in the inactive B chromosomes rDNA clusters suggests these chromosomes can act as a sink for R2 insertions thus further reducing the level of insertions on the A chromosomes. These studies suggest an interesting evolutionary relationship between the parasitic B chromosomes and R2 elements.This study was supported by grants from the Spanish Ministerio de Ciencia y Tecnología (CGL2009-11917) and Plan Andaluz de Investigacion (CVI-6649), and was partially performed by FEDER funds and a grant from the National Institutes of Health (GM42790)

Study and Design of an Immersion Control System for Submarine Sensor Platforms

[Resumen] Las plataformas de sensores son utilizadas como focos de información de ciertos entornos específicos. En el ámbito de los sistemas marítimos y oceanográficos, estas plataformas permiten sensorizar ciertas propiedades del agua a partir de diversas variables, como es el caso del nivel de oxígeno, los niveles de turbidez, clorofila, salinidad, etc. Debido a las diferentes estratificaciones que se producen en este entorno a diferentes profundidades, es necesario realizar la medida a diferentes profundidades. Por ello, este proyecto se va a centrar en el diseño de un algoritmo de control con el fin de gestionar la profundidad de un objeto con capacidad de inmersión, permitiendo así, detener dicha arquitectura sumergible a una profundidad deseada. De manera progresiva se irá analizando el sistema de control, el cual permitirá gestionar la profundidad en función de la posición a la que se encuentren de los actuadores. Para ello, se abordarán diferentes puntos, como es el caso de las consideraciones y características constructivas del modelo, el estudio detallado del comportamiento de cada uno de los principales componentes de sistema, así como de la respuesta de las variables a estudiar. Además, dicho sistema de control se implementará en un microcontrolador con el fin de proporcionar las señales adecuadas en cada instante de tiempo, permitiendo así, que los actuadores introduzcan o desalojen un volumen determinado de agua y, por consiguiente, se logre alcanzar un adecuado y autónomo desplazamiento de la plataforma.[Abstract] Sensor platforms are used as sources of information for certain specific environments. In the field of maritime and oceanographic systems, these platforms make it possible to sensorize certain properties of the water based on various variables, such as the oxygen level, the levels of turbidity, chlorophyll, salinity, etc. Due to the different stratifications that occur in this environment at different depths, it is necessary to perform the measurement at different depths. Therefore, this project will focus on the design of a control algorithm to manage the depth of an object with immersion capacity, thus allowing the submersible architecture to be stopped at a desired depth. The control system will be progressively analyzed, which will allow the depth to be managed depending on the position of the actuators. For this, different points will be addressed, such as the considerations and constructive characteristics of the model, the detailed study of the behavior of each of the main components of the system, as well as the response of the variables to be studied. In addition, said control system will be implemented in a microcontroller to provide the appropriate signals at each instant of time, thus allowing the actuators to introduce or dislodge a specific volume of water and, consequently, achieve an adequate and autonomous platform movement.https://doi.org/10.17979/spudc.9788497498043

Control of Subsea Profiling Platform Using Thrusters and Dynamic Ballast

[Resumen] La laguna del Mar Menor es una de las singularidades ecológicas más importantes del área mediterránea. Sin embargo, es un área donde confluyen muchas actividades económicas e industriales, haciendo así que la suma de los impactos de la minería, la agricultura y el desarrollo urbano alrededor de la laguna durante las últimas décadas, haya afectado considerablemente al ecosistema. Esta situación genera la motivación de este proyecto con el fin de contribuir a la recuperación del Mar Menor desde varios puntos de vista. Uno de ellos es el establecer modelos de comportamiento hidrodinámico para predecir episodios desfavorables que afectan a la laguna salada con el fin de minimizar su impacto. Para poder desarrollar estos modelos, es fundamental monitorizar de forma continua en diferentes puntos y profundidades los parámetros del agua cuya variación afecta al comportamiento general de la laguna. De este modo, surge el objetivo de este proyecto, el cual se centra en la gestión de la profundidad de plataformas de sensores sumergibles. Para ello se han desarrollado e implementado algoritmos de control utilizando un microcontrolador embebido en la propia plataforma. Estos algoritmos gestionan el ascenso y descenso de la plataforma sumergible mediante la variación del peso de la misma a través de actuadores tipo lastre combinados con impulsores dinámicos con el fin de estabilizar la plataforma a una determinada profundidad a la vez que se busca minimizar el consumo energético.[Abstract] The Mar Menor lagoon is one of the most important ecological singularities of the Mediterranean area. However, it is an area where many economic and industrial activities converge, so that the sum of the impacts of mining, agriculture and urban development around the lagoon during the last decades, has considerably affected the ecosystem. This situation generates the motivation for this project in order to contribute to the recovery of the Mar Menor from several points of view. One of them is to establish hydrodynamic behavior models to predict unfavorable episodes that affect the salt lagoon in order to minimize their impact. In order to develop these models, it is essential to continuously monitor at different points and depths the water parameters whose variation affects the overall behavior of the lagoon. Thus, the objective of this project arises, which focuses on the depth management of submersible sensor platforms. For this purpose, control algorithms have been developed and implemented using a microcontroller embedded in the platform itself. These algorithms manage the ascent and descent of the submersible platform by varying the weight of the platform through ballast type actuators combined with dynamic thrusters in order to stabilize the platform at a certain depth while minimizing energy consumption.https://doi.org/10.17979/spudc.978849749841

Estimation of the water stress level in fruit trees using machine learning for applications in intelligent irrigation systems

[Resumen] El agua es un bien escaso, especialmente en las regiones áridas y semiáridas. Este es el caso de la Cuenca Mediterránea, donde sus condiciones demográficas y climáticas la hacen idónea para el cultivo de frutas y hortalizas, demandando un volumen mayor de recursos hídricos. Las estrategias de riego deficitario se han mostrado exitosas, pero resulta primordial el control del estrés hídrico de los cultivos. La medida directa del mismo se encuentra actualmente asociada al potencial hídrico de tallo a mediodía, cuya medida es costosa en tiempo y labores asociadas. A nivel agrario sería interesante definir unos niveles cualitativos del estrés hídrico del cultivo y poder estimarlos a partir de variables cuya medida sea automatizable, de manera que se puedan implementar sistemas de riego inteligente basados en las necesidades hídricas del cultivo. En este trabajo se presenta un estudio preliminar para la obtención de un modelo capaz de predecir cinco niveles de estrés del cultivo a partir de los datos temporales de potencial matricial y contenido volumétrico de agua en el suelo y de diferentes variables agro-climáticas. Se han evaluado múltiples algoritmos de Machine Learning, obteniéndose una precisión máxima en la estimación del 72,4 %.[Abstract] Water is a limited resource in arid and semi-arid regions. This is the case of the Mediterranean area, where its demographic and climatic conditions make it particularly prone to farming, demanding a major percentage of water resources. Deficit irrigation strategies have proved to be successful, but it is essential to control crop water stress. The measurement of crop water stress is currently associated with midday stem water potential, which is very time-consuming. At an agricultural perspective, it would be interesting to define qualitative levels of crop water stress and to be able to estimate them from variables whose measurement can be automated, so that intelligent irrigation systems can be implemented based on the water needs of the crop. In this work we present a preliminary study to obtain a model capable of predicting five levels of crop water stress from time data of water potential and volumetric water content in the soil and different agro-climatic variables. Multiple Machine Learning algorithms have been evaluated, obtaining a maximum estimation accuracy of 72.4%.Ministerio de Ciencia e Innovación; PID2019- 106226-C22/AEI/10.13039/501100011033Ministerio de Asuntos Económicos y Transformación Digital; AGL2016-77282-C33-

Image segmentation of pomegranate fruits using deep learning with application in precision agriculture

[Resumen] En agricultura de precisión, para monitorizar el estado del cultivo mediante imagen de forma automática, son necesarias herramientas de procesamiento para poder extraer la información de interés. En este estudio se desarrolla un modelo de Deep Learning para segmentación de imagen con el objetivo de discriminar los frutos del granado. Se alcanzan unos resultados de Intersection over Union (IoU)=0,71 y mean Average Precision (mAP)=0,82. Posteriormente, se expone un algoritmo que permite estimar el tamaño del fruto en píxeles, con un error relativo medio del 5,4%.[Abstract] In precision agriculture, to automatically monitor the state of the crop using images, processing tools are needed to extract the information of interest. In this study, a Deep Learning model is developed for image segmentation to discriminate pomegranate fruits. Results of Intersection over Union (IoU)=0.71 and mean Average Precision (mAP)=0.82 are achieved. Subsequently, an algorithm for estimating the size of the fruit in pixels is presented, with an average relative error of 5.4%.Este trabajo ha sido financiado por la Agencia Estatal de Investigación (AEI) del Ministerio de Ciencia e Innovación (Convocatoria Retos investigación: Proyectos I+D+i 2017-2020) en el proyecto PID2019-106226RBC2/AEI/10.13039/501100011033. El proyecto aborda la línea prioritaria relacionada con la conservación y gestión eficiente y sostenible de los recursos hídricos. Además, se ha contado con la financiación del Ministerio de Ciencia, Innovación y Universidades: FPU17/05155, FPU19/00020 y EST21/00479

Prediction of Water Stress Episodes in Fruit Trees Based on Soil and Weather Time Series Data

Water is a limited resource in arid and semi-arid regions, as is the case in the Mediterranean Basin, where demographic and climatic conditions make it ideal for growing fruits and vegetables, but a greater volume of water is required. Deficit irrigation strategies have proven to be successful in optimizing available water without pernicious impact on yield and harvest quality, but it is essential to control the water stress of the crop. The direct measurement of crop water status is currently performed using midday stem water potential, which is costly in terms of time and labor; therefore, indirect methods are needed for automatic monitoring of crop water stress. In this study, we present a novel approach to indirectly estimate the water stress of 15-year-old mature sweet cherry trees from a time series of soil water status and meteorological variables by using Machine Learning methods (Random Forest and Support Vector Machine). Time information was accounted for by integrating soil and meteorological measurements within arbitrary periods of 3, 6 and 10 days. Supervised binary classification and regression approaches were applied. The binary classification approach allowed for the definition of a model that alerts the farmer when a dangerous crop water stress episode is about to happen a day in advance. Performance metrics F2 and recall of up to 0.735 and 0.769, respectively, were obtained. With the regression approach a R2 of up to 0.817 was achieved

Prediction of Water Stress Episodes in Fruit Trees Based on Soil and Weather Time Series Data

Water is a limited resource in arid and semi-arid regions, as is the case in the Mediterranean Basin, where demographic and climatic conditions make it ideal for growing fruits and vegetables, but a greater volume of water is required. Deficit irrigation strategies have proven to be successful in optimizing available water without pernicious impact on yield and harvest quality, but it is essential to control the water stress of the crop. The direct measurement of crop water status is currently performed using midday stem water potential, which is costly in terms of time and labor; therefore, indirect methods are needed for automatic monitoring of crop water stress. In this study, we present a novel approach to indirectly estimate the water stress of 15-year-old mature sweet cherry trees from a time series of soil water status and meteorological variables by using Machine Learning methods (Random Forest and Support Vector Machine). Time information was accounted for by integrating soil and meteorological measurements within arbitrary periods of 3, 6 and 10 days. Supervised binary classification and regression approaches were applied. The binary classification approach allowed for the definition of a model that alerts the farmer when a dangerous crop water stress episode is about to happen a day in advance. Performance metrics F2 and recall of up to 0.735 and 0.769, respectively, were obtained. With the regression approach a R2 of up to 0.817 was achieved