Stoebe vulgaris is a declared indigenous bush encroacher species in South Africa. It has invaded over 11 million ha of grasslands. It is commonly called bankrupt bush due to its ability to outcompete other indigenous forb and grass species, decreasing grazing capacity, biodiversity, and ecosystem functioning, eventually leading to financial ruin for farmers. Landowners are legally required to control the plant. A herbicide trial was set up in a severely encroached camp at Dundee Research Station in KwaZulu-Natal, South Africa, to test the effectiveness of metsulfuron-methyl (50 g active ingredient ha−1) in controlling S. vulgaris. Applying metsulfuron-methyl provided a significant long-term reduction in S. vulgaris cover over six years. However, effective monitoring and management strategies depend on knowledge of the spatial distribution and expansion patterns of invasive species. We evaluated the ability of UAV-based imagery and machine learning, using Picterra, to detect and map S. vulgaris, while determining the optimal parameters to maximise detection accuracy. The best season for image acquisition was late summer when vegetation was at peak growth and maturity, providing the best spectral distinction between species, under light overcast and mild wind conditions. We recommend careful consideration of the flight orientation to the solar angle. We achieved 92% detector accuracy, with multispectral imagery enhancing the discrimination of similarly coloured plants. Plants smaller than 10 cm were not detected by the model. Our approach, using high-resolution drone imagery and AI, is capable of individual plant detection suited to a farm scale. This opens the way for using advances in drone technology for targeted, spot-application of herbicide.
In this paper, we offer an overview of the published works dealing with fuzzy logic applied in drones, considering both theoretical works and applications in diverse areas, such as simulation, planning, and control of drones. The analysis was done considering all types of available publications, such as journal papers, chapters, and conference papers. The data were obtained by searching the Scopus database from Elsevier, which contains most of the world’s indexed publications across all areas of knowledge. Based on the obtained data, some conclusions were elaborated about the advances of fuzzy logic and its applications in drones, as well as interesting future trends for this area were delineated. In particular, as fuzzy logic has been evolving from type-1 to type-2 and more recently to type-3, the role of fuzzy systems in the area of drones is following the same evolution. We have to say this evolution has already happened in the area of controlling autonomous mobile robots, and we expect that this will also happen in the area of drones, as the navigation problem is similar to some extent. A limitation of the study is that we are only considering the evolution of fuzzy logic types, rather than other alternatives, such as intuitionistic or hesitant fuzzy theories, which could become more useful in the near future. Also, we are not studying hybrid approaches with fuzzy, like neuro-fuzzy or evolving fuzzy systems, which can be an interesting subject from the point of view of making a fuzzy system to become dynamic or adaptive.
While rare, it is widely accepted that autonomous vehicles (AVs) will find themselves in dilemma scenarios involving vulnerable road users (VRUs). The ethics of these dilemma situations have been debated extensively in the context of trolley-problem-like scenarios. What has not been noted is the inherent unfairness implicit in many of these discussions, in which VRUs are seen as passive bystanders with no say in what befalls them. Rather than simply remaining still in a collision scenario, VRUs can (and often do) take action that needs to be accounted for. If we are to increase fairness on public roads, it is important that AVs communicate with VRUs. This paper presents a highly theoretical discussion on the possibility of using communication tools (such as the V2X system) and techniques (derived from the science of human-machine interaction) to support protective, risk-reducing responses from VRUs during inevitable AV collisions. The paper begins with a brief ethical exploration of fairness in the context of current debates surrounding AV collisions. We proceed to discuss possible technical solutions to AV-VRU communication, as well as the types of audio, visual, and tactile communication strategies necessary in critical scenarios.
Despite a rapid rise of AI-powered Unmanned Aerial Vehicle (UAV) deployments in smart city environments, current surveys and frameworks lack a unified, protocol-level reference architecture that integrates multi-domain applications, edge AI perception, cognitive reasoning through Large Language Models (LLMs), and regulatory compliance within a single deployable specification. This study presents a comprehensive cross-domain review of AI-powered drone systems for traffic management, delivery, infrastructure inspection, disaster response, and environmental monitoring. The study introduces COMPASS (Cognitive Operations Model for Programmable Autonomous Smart-city Systems), a novel seven-layer technical reference architecture that describes communication protocols (MAVLink 2.0, ROS2/DDS, MQTT 5.0, and NGSI-LD), edge computing hardware recommendations for five drone payload tiers, and quantified performance requirements for safety-critical operations. The key feature of COMPASS is its LLM-based Semantic Middleware Layer, which allows for context-aware decision-making, natural human-drone interaction, and regulatory compliance verification. Comparing COMPASS to many other frameworks reveals that it is the only architecture to simultaneously provide multi-domain coverage, protocol-level specifications, hardware recommendations, LLM integration, and empirically verified benchmarks.
Acoustic waves can affect two important components of multi-rotor drones, more formally called multi-rotor unmanned aerial vehicles (UAV). The first is located in the electronic board, the so-called IMU (Inertial Measurement Unit), which can be influenced by intense sound waves at resonant frequency. The second is the motor-propeller unit of drones. Multi-rotor drones generate low-frequency acoustic emissions during flight; if external acoustic waves achieve resonance with these blade-induced vibrations, they can cause structural fatigue or mechanical failure in the motor-propeller unit. The paper addresses the following issues: first, the influence of resonant frequency sound waves on these two design elements and their performance evaluation; second, the feasibility of an integrated counter-UAV system comprising acoustic Direction of Arrival (DoA) estimation and Blade Passage Frequency (BPF) detection; and third, a new solution for a long-range directional sound effector. This proposed solution includes determining the operating frequency as the 3rd to 5th harmonics of the BPF. Furthermore, it introduces a new concept that, instead of using a standard array of sound drivers, utilizes a limited quantity of powerful drivers arranged skeletally according to a Vicsek fractal topology. This configuration generates a powerful, needle-like acoustic beam capable of delivering effective mechanical disruption multi-rotor drones at long ranges.
