Precision Agriculture (PA) is gaining new momentum due to its ability to accurately adjust the resources to a crop’s needs while maintaining/enhancing quality levels. However, crop-damaging pests compromise yields, jeopardizing the benefits of PA. Computer vision-based pest detection techniques offer promising avenues to overcome potential losses for farmers. The recent object detection framework, YOLOv8 (You Only Look Once) applied to real-time insect monitoring is an open-source, cutting-edge PA approach based on Convolutional Neural Network (CNN) models that enables precise and quick decision making in agricultural crops. Under this umbrella, traditional pest studies using YOLO or other deep-learning solutions focused on only one or a few insects for specific crops provide an excessively narrow solution. In this paper, we propose a new form of using YOLO for pest detection with a generalist perspective by intensively testing a YOLOv8-based tool implementing a single insect category. The goal is to detect the presence of any type of insect in any type of crop in real time. A comprehensive performance evaluation is carried out using a well-known dataset. The results of the training, validation, and testing phases are then discussed, obtaining an mAP ₅₀ value of 0.967 for the m model and an mAP _50-95 value of 0.632 for the l model. Finally, we also identify the premises to elaborate a complete and useful dataset able to unleash the full potential of YOLOv8.

Deficient air quality in industrial environments creates a number of problems that affect both the staff and the ecosystems of a particular area. To address this, periodic measurements must be taken to monitor the pollutant substances discharged into the atmosphere. However, the deployed system should also be adapted to the specific requirements of the industry. This paper presents a complete air quality monitoring infrastructure based on the IoT paradigm that is fully integrable into current industrial systems. It includes the development of two highly precise compact devices to facilitate real-time monitoring of particulate matter concentrations and polluting gases in the air. These devices are able to collect other information of interest, such as the temperature and humidity of the environment or the Global Positioning System (GPS) location of the device. Furthermore, machine learning techniques have been applied to the Big Data collected by this system. The results identify that the Gaussian Process Regression is the technique with the highest accuracy among the air quality data sets gathered by the devices. This provides our solution with, for instance, the intelligence to predict when safety levels might be surpassed.

Terahertz-based nano-networks are emerging as a groundbreaking technology able to play a decisive role in future medical applications owing to their ability to precisely quantify figures, such as the viral load in a patient or to predict sepsis shock or heart attacks before they occur. Due to the extremely limited size of the devices composing these nano-networks, the use of the Terahertz (THz) band has emerged as the enabling technology for their communication. However, the characteristics of the THz band, which strictly reduce the communication range inside the human body, together with the energy limitations of nano-nodes make the in-body deployment of nano-nodes a challenging task. To overcome these problems, we propose a novel in-body flow-guided nano-network architecture consisting of three different devices: i) nano-node, ii) nano-router, and iii) bio-sensor. As the performance of this type of nano-network has not been previously explored, a theoretical framework capturing all its particularities is derived to properly model its behavior and evaluate its feasibility in real medical applications. Employing this analytical model, a thorough sensitivity study of its key parameters is accomplished. Finally, we analyze the terahertz flow-guided nano-network design to satisfy the requirements of several medical applications of interest.

Diversity, i.e. transmitting multiple replicas of a signal, may mitigate fading in wireless networks. Among other diversity techniques, the space diversity of multi-antenna systems is particularly interesting since it can complement other forms of diversity. The recent cooperative diversity paradigm brings the advantages of multi-antenna space diversity to single antenna networked devices, which, through cooperation and antenna sharing, form virtual antenna arrays. However, cooperative diversity is a complex technique and research on this topic is still in its early stages. This paper aims at providing a general survey on the theoretical framework; and the physical and medium access control proposals in the literature.