This paper presents an evaluation of different convolutional neural network (CNN) architectures using false-colour images obtained by multispectral sensors on drones for the detection of Black Sigatoka in banana crops. The objective is to use drones to improve the accuracy and efficiency of Black Sigatoka detection to reduce its impact on banana production and improve the sustainable management of banana crops, one of the most produced, traded, and important fruits for food security consumed worldwide. This study aims to improve the precision and accuracy in analysing the images and detecting the presence of the disease using deep learning algorithms. Moreover, we are using drones, multispectral images, and different CNNs, supported by transfer learning, to enhance and scale up the current approach using RGB images obtained by conventional cameras and even smartphone cameras, available in open datasets. The innovation of this study, compared to existing technologies for disease detection in crops, lies in the advantages offered by using drones for image acquisition of crops, in this case, constructing and testing our own datasets, which allows us to save time and resources in the identification of crop diseases in a highly scalable manner. The CNNs used are a type of artificial neural network widely utilised for machine training; they contain several specialised layers interconnected with each other in which the initial layers can detect lines and curves, and gradually become specialised until reaching deeper layers that recognise complex shapes. We use multispectral sensors to create false-colour images around the red colour spectra to distinguish infected leaves. Relevant results of this study include the construction of a dataset with 505 original drone images. By subdividing and converting them into false-colour images using the UAV’s multispectral sensors, we obtained 2706 objects of diseased leaves, 3102 objects of healthy leaves, and an additional 1192 objects of non-leaves to train classification algorithms. Additionally, 3640 labels of Black Sigatoka were generated by phytopathology experts, ideal for training algorithms to detect this disease in banana crops. In classification, we achieved a performance of 86.5% using false-colour images with red, red edge, and near-infrared composition through MobileNetV2 for three classes (healthy leaves, diseased leaves, and non-leaf extras). We obtained better results in identifying Black Sigatoka disease in banana crops using the classification approach with MobileNetV2 as well as our own datasets.

This research supports the possibility of implementing in a smart city, a wireless sensor network, with sensitive nodes in movements implemented in public transport system vehicles and static sink nodes located in a vehicular intersection. The methodology contemplates the influence of mobility in obtaining the parameters of network operation, through mathematical models and probabilistic calculations of the data collected through measurement campaigns carried out in a real traffic light intersection. A model is obtained to calculate the probability of success of the communication based on the speed of the sensitive node and the size of the payload.

We introduce RFEye, a generalized technique to locate signals independent of the waveform, using a single Unmanned Aerial Vehicle (UAV) equipped with only one omnidirectional antenna. This is achieved by acquiring signals from uncoordinated positions within a sphere of 1-meter radius at two nearby locations and formulating asynchronous, distributed receiver beamforming at the UAV to compute the Direction of Arrival (DoA) from the unknown transmitter. The proposed method includes four steps: 1) Blind detection and extraction of unique signature in the signal to be localized, 2) Asynchronous signal acquisition and conditioning, 3) DoA calculation by creating a virtual distributed antenna array at UAV and 4) Obtaining position fix of emitter using DoA from two locations. These steps are analyzed for various sources of error, computational complexity and compared with widely used signal subspace-based DoA estimation algorithms. RFEye is implemented using an Intel-Aero UAV, equipped with a USRP B205 software-defined radio to acquire signals from a ground emitter. Practical outdoor experiments show that RFEye achieves a median accuracy of 1.03m in 2D and 2.5m in 3D for Wi-Fi, and 1.15m in 2D and 2.7m in 3D for LoRa waveforms, and is robust to external factors like wind and UAV position errors.

El aumento exponencial del número de contagios por COVID-19 alrededor del mundo ha originado que los gobiernos, en acompañamiento de comunidades médicas y científicas, recomienden el distanciamiento social de al menos dos metros entre personas, para evitar el crecimiento en el número de contagios. Con el propósito de determinar el cumplimiento de la medida de distanciamiento social, se realiza una prueba piloto mediante el procesamiento a imágenes de video para la detección de personas en espacios abiertos de la zona céntrica de la ciudad de Cúcuta, Colombia. El método propuesto consiste en el análisis de información de videos de personas con fondo estático, tomados desde una altura de 4.5 metros, además de ubicación de centroides por el método de momentos y estimación de distancia por el método euclidiano, esto, mediante lenguaje de programación Python. Como resultado se obtiene un acierto en la detección de personas del 92.43 % y que el 84.21 % de quienes circularon por el cuadro de video y transitan en la zona céntrica de la ciudad de Cúcuta, Colombia, incumplen con el distanciamiento social de dos metros recomendado por la comunidad científica y expuesta en la circular municipal 023 de 2020.

Para el uso eficiente de los recursos y una mejor planificación en la agricultura es necesario tener los datos relacionados con las variables hidroclimáticas del entorno. En este trabajo se propone un sistema electrónico que utiliza el Internet de las Cosas para el monitoreo de variables ambientales como: temperatura, humedad relativa, temperatura del agua, presión atmosférica y nivel de profundidad de la fuente del recurso hídrico. El prototipo se desarrolló utilizando la arquitectura de tres capas: la capa de detección o sensado, la capa de infraestructura o red de comunicación y, la capa del servicio de IoT. Para probar el prototipo se recolectaron 65 muestras para cada variable ambiental en un entorno real, obteniendo: una correlación del 100 % de los datos medidos y almacenados, una eficiencia de transmisión del 95.3 %. Concluyendo que es posible implementar un nodo sensor IoT de bajo costo y fiable.