A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges

In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas. This comprehensive analysis explores the changing landscape of AI-powered UAVs and friendly computing in their applications. It covers emerging trends, futuristic visions, and the inherent challenges that come with this relationship. The study examines how AI plays a role in enabling navigation, detecting and tracking objects, monitoring wildlife, enhancing precision agriculture, facilitating rescue operations, conducting surveillance activities, and establishing communication among UAVs using environmentally conscious computing techniques. By delving into the interaction between AI and UAVs, this analysis highlights the potential for these technologies to revolutionise industries such as agriculture, surveillance practices, disaster management strategies, and more. While envisioning possibilities, it also takes a look at ethical considerations, safety concerns, regulatory frameworks to be established, and the responsible deployment of AI-enhanced UAV systems. By consolidating insights from research endeavours in this field, this review provides an understanding of the evolving landscape of AI-powered UAVs while setting the stage for further exploration in this transformative domain

Assessing the advancement of artificial intelligence and drones’ integration in agriculture through a bibliometric study

Integrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field’s transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era

Fleets of robots for environmentally-safe pest control in agriculture

Feeding the growing global population requires an annual increase in food production. This requirement suggests an increase in the use of pesticides, which represents an unsustainable chemical load for the environment. To reduce pesticide input and preserve the environment while maintaining the necessary level of food production, the efficiency of relevant processes must be drastically improved. Within this context, this research strived to design, develop, test and assess a new generation of automatic and robotic systems for effective weed and pest control aimed at diminishing the use of agricultural chemical inputs, increasing crop quality and improving the health and safety of production operators. To achieve this overall objective, a fleet of heterogeneous ground and aerial robots was developed and equipped with innovative sensors, enhanced end-effectors and improved decision control algorithms to cover a large variety of agricultural situations. This article describes the scientific and technical objectives, challenges and outcomes achieved in three common crops

A Two-Stage Approach for Routing Multiple Unmanned Aerial Vehicles with Stochastic Fuel Consumption

The past decade has seen a substantial increase in the use of small unmanned aerial vehicles (UAVs) in both civil and military applications. This article addresses an important aspect of refueling in the context of routing multiple small UAVs to complete a surveillance or data collection mission. Specifically, this article formulates a multiple-UAV routing problem with the refueling constraint of minimizing the overall fuel consumption for all of the vehicles as a two-stage stochastic optimization problem with uncertainty associated with the fuel consumption of each vehicle. The two-stage model allows for the application of sample average approximation (SAA). Although the SAA solution asymptotically converges to the optimal solution for the two-stage model, the SAA run time can be prohibitive for medium- and large-scale test instances. Hence, we develop a tabu-search-based heuristic that exploits the model structure while considering the uncertainty in fuel consumption. Extensive computational experiments corroborate the benefits of the two-stage model compared to a deterministic model and the effectiveness of the heuristic for obtaining high-quality solutions.Comment: 18 page

Signals in the Soil: Subsurface Sensing

In this chapter, novel subsurface soil sensing approaches are presented for monitoring and real-time decision support system applications. The methods, materials, and operational feasibility aspects of soil sensors are explored. The soil sensing techniques covered in this chapter include aerial sensing, in-situ, proximal sensing, and remote sensing. The underlying mechanism used for sensing is also examined as well. The sensor selection and calibration techniques are described in detail. The chapter concludes with discussion of soil sensing challenges

Monitorización 3D de cultivos y cartografía de malas hierbas mediante vehículos aéreos no tripulados para un uso sostenible de fitosanitarios

En esta Tesis Doctoral se han utilizado las imágenes procedentes de un UAV para abordar la sostenibilidad de la aplicación de productos fitosanitarios mediante la generación de mapas que permitan su aplicación localizada. Se han desarrollado dos formas diferentes y complementarias para lograr este objetivo: 1) la reducción de la aplicación de herbicidas en post-emergencia temprana mediante el diseño de tratamientos dirigidos a las zonas infestadas por malas hierbas en varios cultivos herbáceos; y 2) la caracterización tridimensional (arquitectura y volumen) de cultivos leñosos para el diseño de tratamientos de aplicación localizada de fitosanitarios dirigidos a la parte aérea de los mismos. Para afrontar el control localizado de herbicidas se han estudiado la configuración y las especificaciones técnicas de un UAV y de los sensores embarcados a bordo para su aplicación en la detección temprana de malas hierbas y contribuir a la generación de mapas para un control localizado en tres cultivos herbáceos: maíz, trigo y girasol. A continuación, se evaluaron los índices espectrales más precisos para su uso en la discriminación de suelo desnudo y vegetación (cultivo y malas hierbas) en imágenes-UAV tomadas sobre dichos cultivos en fase temprana. Con el fin de automatizar dicha discriminación se implementó en un entorno OBIA un método de cálculo de umbrales. Finalmente, se desarrolló una metodología OBIA automática y robusta para la discriminación de cultivo, suelo desnudo y malas hierbas en los tres cultivos estudiados, y se evaluó la influencia sobre su funcionamiento de distintos parámetros relacionados con la toma de imágenes UAV (solape, tipo de sensor, altitud de vuelo, momento de programación de los vuelos, entre otros). Por otra parte y para facilitar el diseño de tratamientos fitosanitarios ajustados a las necesidades de los cultivos leñosos se ha desarrollado una metodología OBIA automática y robusta para la caracterización tridimensional (arquitectura y volumen) de cultivos leñosos usando imágenes y modelos digitales de superficies generados a partir de imágenes procedentes de un UAV. Asimismo, se evaluó la influencia de distintos parámetros relacionados con la toma de las imágenes (solape, tipo de sensor, altitud de vuelo) sobre el funcionamiento del algoritmo OBIA diseñado

An investigation of change in drone practices in broadacre farming environments

The application of drones in broadacre farming is influenced by novel and emergent factors. Drone technology is subject to legal, financial, social, and technical constraints that affect the Agri-tech sector. This research showed that emerging improvements to drone technology influence the analysis of precision data resulting in disparate and asymmetrically flawed Ag-tech outputs. The novelty of this thesis is that it examines the changes in drone technology through the lens of entropic decay. It considers the planning and controlling of an organisation’s resources to minimise harmful effects through systems change. The rapid advances in drone technology have outpaced the systematic approaches that precision agriculture insists is the backbone of reliable ongoing decision-making. Different models and brands take data from different heights, at different times of the day, and with flight of differing velocities. Drone data is in a state of decay, no longer equally comparable to past years’ harvest and crop data and are now mixed into a blended environment of brand-specific variations in height, image resolution, air speed, and optics. This thesis investigates the problem of the rapid emergence of image-capture technology in drones and the corresponding shift away from the established measurements and comparisons used in precision agriculture. New capabilities are applied in an ad hoc manner as different features are rushed to market. At the same time existing practices are subtly changed to suit individual technology capability. The result is a loose collection of technically superior drone imagery, with a corresponding mismatch of year-to-year agricultural data. The challenge is to understand and identify the difference between uniformly accepted technological advance, and market-driven changes that demonstrate entropic decay. The goal of this research is to identify best practice approaches for UAV deployment for broadacre farming. This study investigated the benefits of a range of characteristics to optimise data collection technologies. It identified widespread discrepancies demonstrating broadening decay on precision agriculture and productivity. The pace of drone development is so rapidly different from mainstream agricultural practices that the once reliable reliance upon yearly crop data no longer shares statistically comparable metrics. Whilst farmers have relied upon decades of satellite data that has used the same optics, time of day and flight paths for many years, the innovations that drive increasingly smarter drone technologies are also highly problematic since they render each successive past year’s crop metrics as outdated in terms of sophistication, detail, and accuracy. In five years, the standardised height for recording crop data has changed four times. New innovations, coupled with new rules and regulations have altered the once reliable practice of recording crop data. In addition, the cost of entry in adopting new drone technology is sufficiently varied that agriculturalists are acquiring multiple versions of different drone UAVs with variable camera and sensor settings, and vastly different approaches in terms of flight records, data management, and recorded indices. Without addressing this problem, the true benefits of optimization through machine learning are prevented from improving harvest outcomes for broadacre farming. The key findings of this research reveal a complex, constantly morphing environment that is seeking to build digital trust and reliability in an evolving global market in the face of rapidly changing technology, regulations, standards, networks, and knowledge. The once reliable discipline of precision agriculture is now a fractured melting pot of “first to market” innovations and highly competitive sellers. The future of drone technology is destined for further uncertainty as it struggles to establish a level of maturity that can return broadacre farming to consistent global outcomes