Search Results (193)

Consumer-accessible and user-friendly smart products such as unmanned aerial vehicles (UAVs), or drones, have become widely used, adaptable, and acceptable devices to observe, assess, measure, and explore urban and natural environments. A drone’s relatively low cost and flexibility in the level of expertise required to operate it has enabled users from novice to industry professionals to adapt a malleable technology to various disciplines. This review examines the academic literature and maps how drones are currently being used in 93 rural and regional city councils in New South Wales, Australia. Through a systematic review of the academic literature and scrutiny of current drone use in these councils using publicly available information found on council websites, findings reveal potential uses of drone technology for local governments who want to engage with smart technology devices. We looked at how drones were being used in the management of the council’s environment; health and safety initiatives; infrastructure; planning; social and community programmes; and waste and recycling. These findings suggest that drone technology is increasingly being utilised in rural and regional areas. While the focus is on rural and regional New South Wales, a review of the academic literature and local council websites provides a snapshot of drone use examples that holds global relevance for local councils in urban and remote areas seeking to incorporate drone technology into their daily practice of city, town, or region governance. Full article

Mangrove forests are vital blue carbon (BC) ecosystems that significantly contribute to climate change mitigation through carbon sequestration. Accurate, scalable, and cost-effective methods for estimating carbon stocks in these environments are essential for conservation planning. In this study, we assessed the potential of drones, also known as unmanned aerial vehicles (UAVs), for estimating above-ground biomass (AGB) and BC in Avicennia marina stands by integrating drone-based canopy measurements with field-measured tree heights. Using structure-from-motion (SfM) photogrammetry and a consumer-grade drone, we generated a canopy height model and extracted structural parameters from individual trees in the Melgonze mangrove patch, southern Iran. Field-measured tree heights served to validate drone-derived estimates and calibrate an allometric model tailored for A. marina. While drone-based heights differed significantly from field measurements (p < 0.001), the resulting AGB and BC estimates showed no significant difference (p > 0.05), demonstrating that crown area (CA) and model formulation effectively compensate for height inaccuracies. This study confirms that drones can provide reliable estimates of BC through non-invasive means—eliminating the need to harvest, cut, or physically disturb individual trees—supporting their application in mangrove monitoring and ecosystem service assessments, even under challenging field conditions. Full article

Fish kills are sudden mass mortalities that occur in freshwater and marine systems worldwide. Fish kill surveys are essential for assessing the ecological and economic impacts of fish kill events, but are often labor-intensive, time-consuming, and spatially limited. This study aims to address these challenges by exploring the application of unoccupied aerial systems (or drones) and deep learning techniques for coastal fish carcass detection. Seven flights were conducted using a DJI Phantom 4 RGB quadcopter to monitor three sites with different substrates (i.e., sand, rock, shored Sargassum). Orthomosaics generated from drone imagery were useful for detecting carcasses washed ashore, but not floating or submerged carcasses. Single shot multibox detection (SSD) with a ResNet50-based model demonstrated high detection accuracy, with a mean average precision (mAP) of 0.77 and a mean average recall (mAR) of 0.81. The model had slightly higher average precision (AP) when detecting large objects (>42.24 cm long, AP = 0.90) compared to small objects (≤14.08 cm long, AP = 0.77) because smaller objects are harder to recognize and require more contextual reasoning. The results suggest a strong potential future application of these tools for rapid fish kill response and automatic enumeration and characterization of fish carcasses. Full article

The urgent need for affordable and scalable building retrofit solutions has intensified due to stringent clean energy targets. Traditional building energy audits, which are essential in assessing energy performance, are often time-consuming and costly because of the extensive field analysis required. There has been a gradual shift towards the public use of drones, which present opportunities for effective remote procedures that could disrupt a variety of built environment disciplines. Drone-based approaches to data collection offer a great opportunity for the analysis and inspection of existing building stocks, enabling architects, engineers, energy auditors, and owners to document building performance, visualize heat transfer using infrared thermography, and create digital models using 3D photogrammetry. This study provides a review of the potential of a drone-based approach to integrated building envelope assessment, aiming to streamline the process. By evaluating various scanning techniques and their integration with drones, this research explores how drones can enhance data collection for defect identification, as well as digital model creation. A proposed drone-based workflow is tested through a case study in Syracuse, New York, demonstrating its feasibility and effectiveness in creating 3D models and conducting energy simulations. The study also discusses various challenges associated with drone-based approaches, including data accuracy, environmental conditions, operator training, and regulatory compliance, offering practical solutions and highlighting areas for further research. A discussion of the findings underscores the potential of drone technology to revolutionize building inspections, making them more efficient, accurate, and scalable, thus supporting the development of sustainable and energy-efficient buildings. Full article

With the rapid advancement of e-commerce delivery technologies, understanding consumer responses to different types of innovations has become increasingly important. This study examines how consumers react to incremental innovations (e.g., smart lockers) versus radical innovations (e.g., autonomous drones) by integrating personal innovativeness into the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Based on 300 valid survey responses from Chinese consumers and analyzed using structural equation modeling (SEM), the findings demonstrate that personal innovativeness significantly influences key adoption determinants—performance expectancy, effort expectancy, social influence, and facilitating conditions. The adoption of smart lockers is primarily driven by perceived performance and convenience, whereas the adoption of autonomous drones is more strongly shaped by social influence. The proposed model provides both theoretical and practical implications for firms seeking to promote diverse e-commerce delivery technologies. Full article

Drones are widely used in urban air pollution monitoring. Although studies have focused on single-drone applications, collaborative applications for air pollution detection are relatively underexplored. This paper presents a 3D cube-based adaptive cooperative search algorithm that allows two drones to collaborate to explore air pollution. The search space is divided into cubic regions, and each drone explores the upper or lower halves of the cubes and collects data from their vertices. The vertex with the highest measurement is selected by comparing the collected data, and an adjacent cube-shaped search area is generated for exploration. The search continues iteratively until any vertex measurement reaches a predefined threshold. An improved algorithm is also proposed to address the divergence and oscillation that occur during the search. In simulations, the proposed method consumed 21 times less CPU time and required 23 times less search distance compared to linear search. Additionally, the cooperative search method using multiple drones was more efficient than single-drone exploration in terms of the same parameters. Specifically, compared to single-drone exploration, the collaborative drone search reduced CPU time by a factor of 2.6 and search distance by approximately a factor of 2. In experiments in real-world scenarios, multiple drones equipped with the proposed algorithm successfully detected cubes containing air pollution above the threshold level. The findings serve as an important reference for research on drone-assisted target exploration, including air pollution detection. Full article

Precision agriculture is necessary for dealing with problems like pest outbreaks, a lack of water, and declining crop health. Manual inspections and broad-spectrum pesticide application are inefficient, time-consuming, and dangerous. New drone photography and IoT sensors offer quick, high-resolution, multimodal agricultural data collecting. Regional diversity, data heterogeneity, and privacy problems make it hard to conclude these data. This study proposes a lightweight, hybrid deep learning architecture called federated LeViT-ResUNet that combines the spatial efficiency of LeViT transformers with ResUNet’s exact pixel-level segmentation to address these issues. The system uses multispectral drone footage and IoT sensor data to identify real-time insect hotspots, crop health, and yield prediction. The dynamic relevance and sparsity-based feature selector (DRS-FS) improves feature ranking and reduces redundancy. Spectral normalization, spatial–temporal alignment, and dimensionality reduction provide reliable input representation. Unlike centralized models, our platform trains over-dispersed client datasets using federated learning to preserve privacy and capture regional trends. A huge, open-access agricultural dataset from varied environmental circumstances was used for simulation experiments. The suggested approach improves on conventional models like ResNet, DenseNet, and the vision transformer with a 98.9% classification accuracy and 99.3% AUC. The LeViT-ResUNet system is scalable and sustainable for privacy-preserving precision agriculture because of its high generalization, low latency, and communication efficiency. This study lays the groundwork for real-time, intelligent agricultural monitoring systems in diverse, resource-constrained farming situations. Full article

Consumer interest in medicinal and aromatic herbs is on the rise, with buyers increasingly concerned about the microbiological quality of nutraceutical and aromatic plants. The use of Unmanned Aerial Vehicles (UAVs) and sensor technology allows for high-resolution crop monitoring, particularly in the production of rosemary and sage in Grotte (Italy), Agrigento District. The aim of this study is to evaluate the efficacy of UAV-based time series remote sensing data and multimodal data fusion using RGB and multispectral sensors in rosemary and sage harvesting time individuation and the microbiological quality of these nutraceutical and aromatic plants before and after an innovative and sustainable drying process. The multispectral data were acquired with a DJI multispectral camera mounted on a Phantom 4 UAV. The use of drones in the aromatic plant crops can lead to improved efficiency, productivity, and profitability for farmers and businesses. Italian producers follow strict hygiene regulations to reduce bacterial contamination, particularly during the crucial drying process. A rapid drying method at low temperature using a dryer powered by a photovoltaic renewable energy source (RES) helps preserve the quality of the plants. Real-time monitoring of the drying process is enabled through a system based on wireless sensor networks (WSN), providing valuable data on moisture content, drying rates, and microbial stability. Overall, the innovative use of drones, sensor technology, and renewable energy sources in the production of aromatic herbs like rosemary and sage holds great potential for enhancing crop quality, shelf life, and overall sustainability in the chain food industry. Full article

Studies of habitat-related behaviour of mammals are time-consuming. This study aims to develop a model for monitoring the behaviour of mammals in different habitat types using drones mounted with thermal cameras in combination with a YOLO object detection model. Red deer (Cervus elaphus) and fallow deer (Dama dama) were used as model species. The data were collected in the nature reserve, Hanstholm, Northern Denmark. The aim is to develop an AI model capable of distinguishing between four behaviours, “foraging”, “locomoting”, “lying” and “standing”, allowing for insights into the rumination and foraging cycle of the two species. At the same time, the behaviour was linked to habitat types by geocoding individuals. The method developed in this study proved to be time-efficient and provided information about how the two deer species used vegetation types and interspecific interaction between the two species. Technical challenges were to follow individuals and the possibility of missing cyclical behaviour. It was found that the degree to which the ungulates actively foraged was significantly different between the two species and that they were clearly geographically separated within the study area. Full article

Accurate, scalable forestry insights are critical for implementing carbon credit-based reforestation initiatives and data-driven ecosystem management. However, existing forest quantification methods face significant challenges: hand measurement is labor-intensive, time-consuming, and difficult to trust; satellite imagery is not accurate enough; and airborne LiDAR remains prohibitively expensive at scale. In this work, we introduce ForestSplat: an accurate and scalable reforestation monitoring, reporting, and verification (MRV) system built from consumer-grade drone footage and 3D Gaussian Splatting. To evaluate the performance of our approach, we map and reconstruct a 200-acre mangrove restoration project in the Jobos Bay National Estuarine Research Reserve. ForestSplat produces an average mean absolute error (MAE) of 0.17 m and mean error (ME) of 0.007 m compared to canopy height maps derived from airborne LiDAR scans, using 100× cheaper hardware. We hope that our proposed framework can support the advancement of accurate and scalable forestry modeling with consumer-grade drones and computer vision, facilitating a new gold standard for reforestation MRV. Full article

Accurately predicting rice yield and its components is crucial for optimizing agricultural practices and ensuring food security. Traditional methods of assessing crop status wwcan be time-consuming and labor-intensive. This study investigated the use of drone-based multispectral imagery and machine learning to improve the prediction of rice yield and yield components. Time-series VIs were collected from 152 rice samples across various nitrogen treatments, transplanting times, and rice varieties in 2023 and 2024, using an UAV at approximately 3-day intervals. A four-parameter log-normal model was applied to analyze the VI curves, effectively quantifying the maximum value, spread, and baseline of each index, revealing the dynamic influence of nitrogen and transplanting timing on crop growth. Machine learning regression models were then used to predict yield and yield components using the log-normal parameters and individual VIs as input. Results showed that the maximum (a) and variance (c) parameters of the log-normal model, derived from the VI curves, were strongly correlated with yield, grain number, and panicle number, emphasizing the importance of mid-to-late growth stages. Among the tested VIs, NDRE, LCI, and NDVI demonstrated the highest accuracy in predicting yield and key yield components. This study demonstrates that integrating log-normal modeling of time-series multispectral data with machine learning provides a powerful and efficient approach for precision agriculture, enabling more accurate and timely assessments of rice yield and its contributing factors. Full article

Point cloud-based BIM reconstruction is an effective approach to enabling the digital documentation of existing buildings. However, current methods often demand substantial time and expertise for the manual measurement of building dimensions and the drafting of BIMs. This paper proposes an automated approach to BIM modeling of the external surfaces of existing buildings, aiming to streamline the labor-intensive and time-consuming processes of manual measurement and drafting. Initially, multi-angle images of the building are captured using drones, and the building’s point cloud is reconstructed using 3D reconstruction software. Next, a multi-plane segmentation technique based on the RANSAC algorithm is applied, facilitating the efficient extraction of key features of exterior walls and planar roofs. The orthophotos of the building façades are generated by projecting wall point clouds onto a 2D plane. A lightweight convolutional encoder–decoder model is utilized for the semantic segmentation of windows and doors on the façade, enabling the precise extraction of window and door features and the automated generation of AutoCAD elevation drawings. Finally, the extracted features and segmented data are integrated to generate the BIM. The case study results demonstrate that the proposed method exhibits a stable error distribution, with model accuracy exceeding architectural industry requirements, successfully achieving reliable BIM reconstruction. However, this method currently faces limitations in dealing with buildings with complex curved walls and irregular roof structures or dense vegetation obstacles. Full article

The paper provides an in-depth investigation of techniques for improving small parcel delivery services in a private logistics company, addressing significant difficulties in customer logistics service, particularly in the growing e-commerce industry. The study addresses a gap in the existing literature by assessing 170 documented customer complaints, with an emphasis on recurring issues such as improper delivery, delays, and damaged parcels. The methodological approach uses statistical tools to determine the magnitude of delivery challenges, integrating a review of the scientific literature with real data analysis. There are 28% complaints about faulty delivery and 26% about delays, according to the statistics. It is clear that systemic improvements are urgently needed. One strategy to improve service reliability and efficiency is to use automation technologies, such as drones, smart route optimization systems, and constant human training programs. While ensuring operational sustainability, these strategies aim to address the underlying causes of consumer dissatisfaction. Full article

The fuel consumption of a harvester–operator system was modeled to select logging methods by comparing the forward felling technique (C) and the sideways techniques at the logging edge (A and D) or inside of the stand (B and E). To that end, trees’ logging cycle process data were collected using a drone for time consumption analysis. The fuel consumption data were recorded automatically from the harvester’s digital monitoring system. The fuel consumption averaged 0.22 L during the logging cycle process of trees on flat terrain and 0.25 L for those on sloping terrain. In stands on flat terrain, logging method C consumed 7.9 L E₀h⁻¹ more fuel than method A and 4.9 L E₀h⁻¹ more fuel than method B, meaning method A consumed 3.0 L E₀h⁻¹ less fuel than method B. On sloping terrain, logging method D consumed 1.4 L E₀h⁻¹ less fuel than method E. There was a large variation in fuel consumption between the logging methods, which was explained most efficiently (R² = 0.70) by the stem processing speed (m E₀h⁻¹), the tree’s stem length (m), and effective hours of tree logging cycle processes (E₀h). The results reveal that logging methods A and D were the most efficient. This precision modeling structure is recommended for the development of working techniques for harvester operators and for environmental efficiency comparisons of logging methods in different timber harvesting conditions. Full article

This study presents a cooperative system where drones and ground robots share information to efficiently complete tasks in environments that challenge the capabilities of a single robot. Drones focus on exploring high-interest areas for ground robots, generating occupancy grid maps and identifying high-risk routes. Ground robots use this information to evaluate and adapt routes as needed. Flexible action planning through behavior trees enables the robots to respond dynamically to environmental changes, facilitating spontaneous and adaptable cooperation. Experiments with real robots confirmed the system’s performance and adaptability to various settings. Specifically, when high-risk areas were identified from drone provided information, ground robots generated alternative routes to bypass these zones, demonstrating the system’s capacity to navigate complex paths while minimizing risks. This establishes a basis for scaling to larger environments. The proposed system is expected to improve the safety and efficiency of robot operations by enabling multiple robots to accomplish complex tasks collaboratively-tasks that would be difficult or time consuming for an individual robot. The findings demonstrate the potential for multi-robot cooperation to enhance task execution in challenging environments and provide a framework for future research on effective role sharing and information exchange in autonomous systems. Full article