Siliang Chen¹, Dongri Shan^2,*, Yansheng Niu³

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5263-5286, 2025, DOI:10.32604/cmc.2025.067892 - 23 October 2025

Abstract With the increasing deployment of Unmanned Aerial Vehicle-Hangar (UAV-H) clusters in dynamic environments such as disaster response and precision agriculture, existing networking schemes often struggle with adaptability to complex scenarios, while traditional Vertical Handoff (VHO) algorithms fail to fully address the unique challenges of UAV-H systems, including high-speed mobility and limited computational resources. To bridge this gap, this paper proposes a heterogeneous network architecture integrating 5th Generation Mobile Communication Technology (5G) cellular networks and self-organizing mesh networks for UAV-H clusters, accompanied by a novel VHO algorithm. The proposed algorithm leverages Multi-Attribute Decision-Making (MADM) theory combined… More >

Sadia Alam Shammi^1,2, Yanbo Huang^1,*, Weiwei Xie^1,2, Gary Feng¹, Haile Tewolde¹, Xin Zhang³, Johnie Jenkins¹, Mark Shankle⁴

Phyton-International Journal of Experimental Botany, Vol.94, No.9, pp. 2745-2766, 2025, DOI:10.32604/phyton.2025.068955 - 30 September 2025

Abstract Crop leaf area index (LAI) and biomass are two major biophysical parameters to measure crop growth and health condition. Measuring LAI and biomass in field experiments is a destructive method. Therefore, we focused on the application of unmanned aerial vehicles (UAVs) in agriculture, which is a cost and labor-efficient method. Hence, UAV-captured multispectral images were applied to monitor crop growth, identify plant bio-physical conditions, and so on. In this study, we monitored soybean crops using UAV and field experiments. This experiment was conducted at the MAFES (Mississippi Agricultural and Forestry Experiment Station) Pontotoc Ridge-Flatwoods Branch… More >

Weicong Tan^1,#, Qiwu Wu^2,3,#,*, Lingzhi Jiang¹, Tao Tong², Yunchen Su²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3621-3652, 2025, DOI:10.32604/cmc.2025.068781 - 23 September 2025

Abstract This study introduces a novel algorithm known as the dung beetle optimization algorithm based on bounded reflection optimization and multi-strategy fusion (BFDBO), which is designed to tackle the complexities associated with multi-UAV collaborative trajectory planning in intricate battlefield environments. Initially, a collaborative planning cost function for the multi-UAV system is formulated, thereby converting the trajectory planning challenge into an optimization problem. Building on the foundational dung beetle optimization (DBO) algorithm, BFDBO incorporates three significant innovations: a boundary reflection mechanism, an adaptive mixed exploration strategy, and a dynamic multi-scale mutation strategy. These enhancements are intended to… More >

Khadija Slimani^1,2,*, Samira Khoulji², Hamed Taherdoost^3,4, Mohamed Larbi Kerkeb⁵

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4115-4132, 2025, DOI:10.32604/cmc.2025.066544 - 23 September 2025

Abstract Reliable and efficient communication is essential for Unmanned Aerial Vehicle (UAV) networks, especially in dynamic and resource-constrained environments such as disaster management, surveillance, and environmental monitoring. Frequent topology changes, high mobility, and limited energy availability pose significant challenges to maintaining stable and high-performance routing. Traditional routing protocols, such as Ad hoc On-Demand Distance Vector (AODV), Load-Balanced Optimized Predictive Ad hoc Routing (LB-OPAR), and Destination-Sequenced Distance Vector (DSDV), often experience performance degradation under such conditions. To address these limitations, this study evaluates the effectiveness of Dynamic Adaptive Routing (DAR), a protocol designed to adapt routing decisions… More >

Lei Lei¹, Chengfu Wu^2,*, Huaimin Chen²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1339-1363, 2025, DOI:10.32604/cmc.2025.067117 - 29 August 2025

Abstract This paper proposes a Multi-Agent Attention Proximal Policy Optimization (MA2PPO) algorithm aiming at the problems such as credit assignment, low collaboration efficiency and weak strategy generalization ability existing in the cooperative pursuit tasks of multiple unmanned aerial vehicles (UAVs). Traditional algorithms often fail to effectively identify critical cooperative relationships in such tasks, leading to low capture efficiency and a significant decline in performance when the scale expands. To tackle these issues, based on the proximal policy optimization (PPO) algorithm, MA2PPO adopts the centralized training with decentralized execution (CTDE) framework and introduces a dynamic decoupling mechanism,… More >

Kelvin Kang Wee Tang^1,*, Muhammad Hafiz Mohd Yatim¹, Norhadija Darwin², Wan Anom Wan Aris¹, Sim Ching Yen³, Nurfazira Mohamed Fadil³

Revue Internationale de Géomatique, Vol.34, pp. 587-602, 2025, DOI:10.32604/rig.2025.065583 - 06 August 2025

Abstract The integration of Unmanned Aerial Vehicles (UAVs) and Uncrewed Surface Vehicles (USVs) has revolutionized topographic and bathymetric mapping, significantly enhancing the accuracy and efficiency of geospatial data acquisition processes. This innovative approach synergistically combines terrestrial data collected by UAVs with underwater data obtained through USVs, culminating in the creation of unified high-resolution Digital Elevation Models (DEMs) of the river basin region represents a vital step toward understanding the dynamic interactions between land and water bodies. Hence, the seamless Topo-Bathymetric Elevation Model offers a detailed perspective of the river system, supporting informed decision-making in addressing sediment… More >

E. M. B. M. Karunathilake^1,#, Thanh Tuan Thai^1,2,3,#, Sheikh Mansoor¹, Anh Tuan Le^3,4, Faheem Shehzad Baloch^1,5, Yong Suk Chung^1,*, Dong-Wook Kim^6,*

Phyton-International Journal of Experimental Botany, Vol.94, No.7, pp. 2215-2228, 2025, DOI:10.32604/phyton.2025.067439 - 31 July 2025

Abstract Accurate and timely estimation of above-ground biomass is crucial for understanding crop growth dynamics, optimizing agricultural input management, and assessing productivity in sustainable farming practices. However, conventional biomass assessments are destructive and resource-intensive. In contrast, remote sensing techniques, particularly those utilizing low-altitude unmanned aerial vehicles, provide a non-destructive approach to collect imagery data on plant canopy features, including spectral reflectance and structural details at any stage of the crop life cycle. This study explores the potential visible-light-derived vegetative indices to improve biomass prediction during the flowering period of buckwheat (Fagopyrum tataricum). Red, green, and blue (RGB)… More >

Ching-Lung Fan^1,*, Yu-Jen Chung², Shan-Min Yen^1,3

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4627-4653, 2025, DOI:10.32604/cmc.2025.066856 - 30 July 2025

Abstract Efficient banana crop detection is crucial for precision agriculture; however, traditional remote sensing methods often lack the spatial resolution required for accurate identification. This study utilizes low-altitude Unmanned Aerial Vehicle (UAV) images and deep learning-based object detection models to enhance banana plant detection. A comparative analysis of Faster Region-Based Convolutional Neural Network (Faster R-CNN), You Only Look Once Version 3 (YOLOv3), Retina Network (RetinaNet), and Single Shot MultiBox Detector (SSD) was conducted to evaluate their effectiveness. Results show that RetinaNet achieved the highest detection accuracy, with a precision of 96.67%, a recall of 71.67%, and… More >

Abdulwahab Alazeb¹, Muhammad Hanzla², Naif Al Mudawi^1,*, Mohammed Alshehri¹, Haifa F. Alhasson³, Dina Abdulaziz AlHammadi⁴, Ahmad Jalal^2,5

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4677-4697, 2025, DOI:10.32604/cmc.2025.065899 - 30 July 2025

Abstract Unmanned Aerial Vehicles (UAVs) have become indispensable for intelligent traffic monitoring, particularly in low-light conditions, where traditional surveillance systems struggle. This study presents a novel deep learning-based framework for nighttime aerial vehicle detection and classification that addresses critical challenges of poor illumination, noise, and occlusions. Our pipeline integrates MSRCR enhancement with OPTICS segmentation to overcome low-light challenges, while YOLOv10 enables accurate vehicle localization. The framework employs GLOH and Dense-SIFT for discriminative feature extraction, optimized using the Whale Optimization Algorithm to enhance classification performance. A Swin Transformer-based classifier provides the final categorization, leveraging hierarchical attention mechanisms More >

Naif Al Mudawi^1,*, Muhammad Hanzla², Abdulwahab Alazeb¹, Mohammed Alshehri¹, Haifa F. Alhasson³, Dina Abdulaziz AlHammadi⁴, Ahmad Jalal^2,5

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4491-4509, 2025, DOI:10.32604/cmc.2025.065490 - 30 July 2025

Abstract Unmanned Aerial Vehicles (UAVs) are increasingly employed in traffic surveillance, urban planning, and infrastructure monitoring due to their cost-effectiveness, flexibility, and high-resolution imaging. However, vehicle detection and classification in aerial imagery remain challenging due to scale variations from fluctuating UAV altitudes, frequent occlusions in dense traffic, and environmental noise, such as shadows and lighting inconsistencies. Traditional methods, including sliding-window searches and shallow learning techniques, struggle with computational inefficiency and robustness under dynamic conditions. To address these limitations, this study proposes a six-stage hierarchical framework integrating radiometric calibration, deep learning, and classical feature engineering. The workflow… More >