Solar energy radiation measurement with a low–power solar energy harvester

Solar energy radiation measurements are essential in precision agriculture and forest monitoring and can be readily performed by attaching commercial pyranometers to autonomous sensor nodes. However this solution significantly increases power consumption up to tens of milliwatts and can cost hundreds of euros. Since many autonomous sensor nodes are supplied from photovoltaic (PV) panels which currents depend on solar irradiance, we propose to double PV panels as solar energy sensors. In this paper, the inherent operation of the low-power solar energy harvester of a sensor node is also used to measure the open circuit voltage and the current at the maximum power point (IMPP), which allows us to determine solar irradiance and compensate for its temperature drift. The power consumption and cost added to the original solar energy harvester are minimal. Experimental results show that the relation between the measured IMPP and solar irradiance is linear for radiation above 50¿W/m2, and the relative uncertainty limit achieved for the slope is ±2.4% due the light spectra variation. The relative uncertainty limit of daily solar insolation is below ±3.6% and is hardly affected by the so called cosine error, i.e. the error caused by reflection and absorption of light in PV panel surface.Peer ReviewedPostprint (author's final draft

Articles publicats en accés obert al 2018 al Campus del Baix Llobregat

Amb motiu de la setmana mundial de l'accés obert (Open Access Week 2019) presentem aquest document amb els articles publicats en accés obert publicats al 2018 des del Campus del Baix Llobregat a Castelldefels.Postprint (published version

A Multi-Sensor Phenotyping System: Applications on Wheat Height Estimation and Soybean Trait Early Prediction

Phenotyping is an essential aspect for plant breeding research since it is the foundation of the plant selection process. Traditional plant phenotyping methods such as measuring and recording plant traits manually can be inefficient, laborious and prone to error. With the help of modern sensing technologies, high-throughput field phenotyping is becoming popular recently due to its ability of sensing various crop traits non-destructively with high efficiency. A multi-sensor phenotyping system equipped with red-green-blue (RGB) cameras, radiometers, ultrasonic sensors, spectrometers, a global positioning system (GPS) receiver, a pyranometer, a temperature and relative humidity probe and a light detection and ranging (LiDAR) was first constructed, and a LabVIEW program was developed for sensor controlling and data acquisition. Two studies were conducted focusing on system performance examination and data exploration respectively. The first study was to compare wheat height measurements from ultrasonic sensor and LiDAR. Canopy heights of 100 wheat plots were estimated five times over the season by the ground phenotyping system, and the results were compared to manual measurements. Overall, LiDAR provided the better estimations with root mean square error (RMSE) of 0.05 m and R2 of 0.97. Ultrasonic sensor did not perform well due to the style of our application. In conclusion LiDAR was recommended as a reliable method for wheat height evaluation. The second study was to explore the possibility of early predicting soybean traits through color and texture features of canopy images. Six thousand three hundred and eighty-three RGB images were captured at V4/V5 growth stage over 5667 soybean plots growing at four locations. One hundred and forty color features and 315 gray-level co-occurrence matrix (GLCM)-based texture features were derived from each image. Another two variables were also introduced to account for the location and timing difference between images. Cubist and Random Forests were used for regression and classification modelling respectively. Yield (RMSE=9.82, R2=0.68), Maturity (RMSE=3.70, R2=0.76) and Seed Size (RMSE=1.63, R2=0.53) were identified as potential soybean traits that might be early-predictable. Advisor: Yufeng G

A Multi-Sensor Phenotyping System: Applications on Wheat Height Estimation and Soybean Trait Early Prediction

Phenotyping is an essential aspect for plant breeding research since it is the foundation of the plant selection process. Traditional plant phenotyping methods such as measuring and recording plant traits manually can be inefficient, laborious and prone to error. With the help of modern sensing technologies, high-throughput field phenotyping is becoming popular recently due to its ability of sensing various crop traits non-destructively with high efficiency. A multi-sensor phenotyping system equipped with red-green-blue (RGB) cameras, radiometers, ultrasonic sensors, spectrometers, a global positioning system (GPS) receiver, a pyranometer, a temperature and relative humidity probe and a light detection and ranging (LiDAR) was first constructed, and a LabVIEW program was developed for sensor controlling and data acquisition. Two studies were conducted focusing on system performance examination and data exploration respectively. The first study was to compare wheat height measurements from ultrasonic sensor and LiDAR. Canopy heights of 100 wheat plots were estimated five times over the season by the ground phenotyping system, and the results were compared to manual measurements. Overall, LiDAR provided the better estimations with root mean square error (RMSE) of 0.05 m and R2 of 0.97. Ultrasonic sensor did not perform well due to the style of our application. In conclusion LiDAR was recommended as a reliable method for wheat height evaluation. The second study was to explore the possibility of early predicting soybean traits through color and texture features of canopy images. Six thousand three hundred and eighty-three RGB images were captured at V4/V5 growth stage over 5667 soybean plots growing at four locations. One hundred and forty color features and 315 gray-level co-occurrence matrix (GLCM)-based texture features were derived from each image. Another two variables were also introduced to account for the location and timing difference between images. Cubist and Random Forests were used for regression and classification modelling respectively. Yield (RMSE=9.82, R2=0.68), Maturity (RMSE=3.70, R2=0.76) and Seed Size (RMSE=1.63, R2=0.53) were identified as potential soybean traits that might be early-predictable. Advisor: Yufeng G

Diseño e implementación de nodo autónomo IoT para una red de área amplia de bajo consumo (LPWAN): aplicación a la gestión de luminarias urbanas inteligentes

[ES] Para lograr la gestión efectiva de sistemas constituidos por numerosos elementos dispersos sin conectividad a internet por su ubicación en zonas rurales, aisladas o despobladas, resulta de gran importancia encontrar soluciones de interconexión alternativas a las convencionales. Se plantea el uso de redes en forma de malla que hagan uso de tecnologías LPWAN. Este tipo de red estaría constituido por múltiples nodos aislados y autónomos, que tengan la posibilidad de comunicarse con el resto de nodos, o con una puerta de enlace que dote a toda la red de conectividad a internet. La comunicación con los nodos permitiría la gestión, vigilancia y monitorización en tiempo real, y de forma remota, de las aplicaciones en las que se utilice. En este trabajo se va a realizar el diseño e implementación de uno de estos nodos atendiendo a los requisitos de las aplicaciones previstas. Pueden encontrarse aplicaciones para este dispositivo en distintos ámbitos: para usos agrícolas y ganaderos sería útil como dispositivo de seguimiento para ganado, telemando de válvulas para riego o administración de químicos, monitorización de condiciones del terreno, alerta temprana contra incendios o robo de cosechas; en ciudades inteligentes para gestión de luminarias, de aparcamientos, monitorización de calidad del aire y nivel de CO2 en locales comerciales. El objetivo es diseñar un dispositivo inteligente, autónomo, de bajo consumo energético, que pueda transmitir bajo volumen de datos a largas distancias y con capacidad de conocer y actuar con elementos del entorno. La solución adoptada debe ser una solución integrada y escalable, a la vez que flexible, para permitir su uso en distintas aplicaciones. Además, el hecho de que, una vez el dispositivo diseñado haya sido desplegado en el campo, se encuentre en localizaciones aisladas y en situación desatendida, obliga a tomar en consideración que las posibilidades de realizar mantenimiento serán limitadas, aun cuando se encuentre en condiciones hostiles, como son la exposición a la lluvia, el polvo o la intemperie. El resultado del proyecto será una tarjeta de circuito impreso funcional, basada en un microcontrolador, que cumpla los objetivos expuestos. Posteriormente se fabricará en el laboratorio una maqueta de aplicación para smart lighting, en la que se gestione una luminaria LED y se realizarán pruebas de campo con la tarjeta para verificar el éxito del proyecto.[EN] To achieve effective management of systems made up of a set of dispersed elements without internet connectivity due to their location in rural, isolated or unpopulated areas, it is of great importance to find alternative interconnection methods to conventional ones. In this work is proposed the use of mesh networks that make use of Low Power Wide Area Network (LPWAN) technologies. This type of network would be made up of multiple isolated and autonomous nodes, which have the possibility of communicating with the rest of the nodes, or with a gateway that provides internet connectivity to the entire network. The communication with the nodes would allow the management, surveillance and monitoring in real time, and remotely, of the applications in which it is used. The design and implementation of one of these nodes will be carried out in this work. This will be done taking into account the requirements of the planned applications. Applications for this device can be found in many different areas: for agricultural and livestock uses it would be useful as a tracking device for livestock, remote control of valves for irrigation or for chemical administration, ground condition monitoring, early warning against fires or theft of crops; in smart cities for lighting or parking management, air quality and CO2 level monitoring. The goal is to design an intelligent stand-alone device, with low energy consumption, which can transmit a low volume of data over long distances, and with the ability to know and act with elements of the environment. The adopted solution has to be an integrated and scalable one, as well as flexible, to allow its use in different applications. In addition, the fact that once the designed device has been deployed in isolated locations and in an unattended situation, makes it necessary to take into account that the possibility of carrying out maintenance works will be very limited. This is a matter of great importance if the device is exposed to hostile environments such as rain, dust or bad weather situations. The result of this work will be a functional printed circuit board based on a microcontroller that meets the stated goals. Subsequently, an application model for smart lighting will be set-up in the laboratory; a test fixture in which an LED luminaire is managed by means of the designed device. Field tests will be carried out with the manufactured device to verify the success of the project.Argente Garrigós, J. (2021). Diseño e implementación de nodo autónomo IoT para una red de área amplia de bajo consumo (LPWAN): aplicación a la gestión de luminarias urbanas inteligentes. Universitat Politècnica de València. http://hdl.handle.net/10251/175397TFG