Zhuoyan Xie¹, Qi Wang^1,*, Bin Kong^2,*, Shang Gao¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3013-3027, 2025, DOI:10.32604/cmc.2025.064147 - 03 July 2025

Abstract In the current era of intelligent technologies, comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring, emergency rescue, and agricultural plant protection. Owing to their exceptional flexibility and rapid deployment capabilities, unmanned aerial vehicles (UAVs) have emerged as the ideal platforms for accomplishing these tasks. This study proposes a swarm A*-guided Deep Q-Network (SADQN) algorithm to address the coverage path planning (CPP) problem for UAV swarms in complex environments. Firstly, to overcome the dependency of traditional modeling methods on regular terrain environments, this study proposes an improved cellular decomposition… More >

Fuheng Qu¹, Hailong Li^1,*, Ping Wang², Sike Guo², Lu Wang², Xiaofeng Li^3,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3911-3925, 2025, DOI:10.32604/cmc.2025.063820 - 03 July 2025

Abstract Rice spike detection and counting play a crucial role in rice yield research. Automatic detection technology based on Unmanned Aerial Vehicle (UAV) imagery has the advantages of flexibility, efficiency, low cost, safety, and reliability. However, due to the complex field environment and the small target morphology of some rice spikes, the accuracy of detection and counting is relatively low, and the differences in phenotypic characteristics of rice spikes at different growth stages have a significant impact on detection results. To solve the above problems, this paper improves the You Only Look Once v8 (YOLOv8) model,… More >

Zhe Chen^*, Yinyang Zhang, Sihao Xing

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1787-1803, 2025, DOI:10.32604/cmc.2025.065238 - 09 June 2025

Abstract Unmanned aerial vehicle (UAV) imagery poses significant challenges for object detection due to extreme scale variations, high-density small targets (68% in VisDrone dataset), and complex backgrounds. While YOLO-series models achieve speed-accuracy trade-offs via fixed convolution kernels and manual feature fusion, their rigid architectures struggle with multi-scale adaptability, as exemplified by YOLOv8n’s 36.4% mAP and 13.9% small-object AP on VisDrone2019. This paper presents YOLO-LE, a lightweight framework addressing these limitations through three novel designs: (1) We introduce the C2f-Dy and LDown modules to enhance the backbone’s sensitivity to small-object features while reducing backbone parameters, thereby improving More >

Reem Alkanhel^1,#, Abuzar B. M. Adam^2,#, Samia Allaoua Chelloug¹, Dina S. M. Hassan^1,*, Mohammed Saleh Ali Muthanna³, Ammar Muthanna⁴

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 607-624, 2025, DOI:10.32604/cmc.2025.063206 - 09 June 2025

Abstract The 3GPP standard defines the requirements for next-generation wireless networks, with particular attention to Ultra-Reliable Low-Latency Communications (URLLC), critical for applications such as Unmanned Aerial Vehicles (UAVs). In this context, Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technique to improve spectrum efficiency and user fairness by allowing multiple users to share the same frequency resources. However, optimizing key parameters–such as beamforming, rate allocation, and UAV trajectory–presents significant challenges due to the nonconvex nature of the problem, especially under stringent URLLC constraints. This paper proposes an advanced deep learning-driven approach to address the resulting… More >

Jackson Silva Nóbrega^1,*, Geovani Soares de Lima¹, Jean Telvio Andrade Ferreira², Julio Cesar Agostinho da Silva¹, Lauriane Almeida dos Anjos Soares¹, Valéria Fernandes de Oliveira Sousa¹, Paulo Vinicius de Oliveira Freire¹, Reynaldo Teodoro de Fátima¹, Flávia de Sousa Almeida¹, Hans Raj Gheyi², Josemir Moura Maia³

Phyton-International Journal of Experimental Botany, Vol.94, No.5, pp. 1587-1600, 2025, DOI:10.32604/phyton.2025.063633 - 29 May 2025

Abstract The Northeast region is the main producer of guava in Brazil, generating employment and income. However, water availability means that producer’s resort to using water with high salinity, which harms plant development, especially during the seedling formation phase. The adoption of techniques that mitigate the deleterious effect of salinity is increasingly necessary, such as the use of elicitors such as ascorbic acid. The purpose of this study was to analyze the morphophysiology of guava seedlings under saline and ascorbic acid levels. The study was carried out by applying treatments composed of five saline levels (SL… More >

Trong Hieu Luu¹, Phan Nguyen Ky Phuc², Quang Hieu Ngo^1,*, Thanh Tam Nguyen³, Huu Cuong Nguyen¹

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5643-5656, 2025, DOI:10.32604/cmc.2025.064007 - 19 May 2025

Abstract This study presents a drone-based aerial imaging method for automated rice seedling detection and counting in paddy fields. Utilizing a drone equipped with a high-resolution camera, images are captured 14 days post-sowing at a consistent altitude of six meters, employing autonomous flight for uniform data acquisition. The approach effectively addresses the distinct growth patterns of both single and clustered rice seedlings at this early stage. The methodology follows a two-step process: first, the GoogleNet deep learning network identifies the location and center points of rice plants. Then, the U-Net deep learning network performs classification and… More >

Huiru Cao¹, Shaoxin Li², Xiaomin Li^3,*, Yongxin Liu⁴

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5575-5593, 2025, DOI:10.32604/cmc.2025.061954 - 19 May 2025

Abstract Efficient flight path design for unmanned aerial vehicles (UAVs) in urban environmental event monitoring remains a critical challenge, particularly in prioritizing high-risk zones within complex urban landscapes. Current UAV path planning methodologies often inadequately account for environmental risk factors and exhibit limitations in balancing global and local optimization efficiency. To address these gaps, this study proposes a hybrid path planning framework integrating an improved Ant Colony Optimization (ACO) algorithm with an Orthogonal Jump Point Search (OJPS) algorithm. Firstly, a two-dimensional grid model is constructed to simulate urban environments, with key monitoring nodes selected based on… More >

Wenli Lei^1,2,*, Xinghao Wu^1,2, Kun Jia^1,2, Jinping Han^1,2

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5679-5698, 2025, DOI:10.32604/cmc.2025.061268 - 19 May 2025

Abstract Aiming to address the limitations of the standard Chimp Optimization Algorithm (ChOA), such as inadequate search ability and susceptibility to local optima in Unmanned Aerial Vehicle (UAV) path planning, this paper proposes a three-dimensional path planning method for UAVs based on the Improved Chimp Optimization Algorithm (IChOA). First, this paper models the terrain and obstacle environments spatially and formulates the total UAV flight cost function according to the constraints, transforming the path planning problem into an optimization problem with multiple constraints. Second, this paper enhances the diversity of the chimpanzee population by applying the Sine… More >

Honglin Wang¹, Yaolong Zhang^2,*, Cheng Zhu³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1929-1949, 2025, DOI:10.32604/cmc.2025.061363 - 16 April 2025

Abstract UAV-based object detection is rapidly expanding in both civilian and military applications, including security surveillance, disaster assessment, and border patrol. However, challenges such as small objects, occlusions, complex backgrounds, and variable lighting persist due to the unique perspective of UAV imagery. To address these issues, this paper introduces DAFPN-YOLO, an innovative model based on YOLOv8s (You Only Look Once version 8s). The model strikes a balance between detection accuracy and speed while reducing parameters, making it well-suited for multi-object detection tasks from drone perspectives. A key feature of DAFPN-YOLO is the enhanced Drone-AFPN (Adaptive Feature… More >

Can Wu^1,2, Wenyi Tang², Yunbo Rao^1,2,*, Yinjie Chen¹, Hui Ding², Shuzhen Zhu³, Yuanyuan Wang³

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1415-1434, 2025, DOI:10.32604/cmc.2025.059797 - 26 March 2025

Abstract Infrared unmanned aerial vehicle (UAV) target detection presents significant challenges due to the interplay between small targets and complex backgrounds. Traditional methods, while effective in controlled environments, often fail in scenarios involving long-range targets, high noise levels, or intricate backgrounds, highlighting the need for more robust approaches. To address these challenges, we propose a novel three-stage UAV segmentation framework that leverages uncertainty quantification to enhance target saliency. This framework incorporates a Bayesian convolutional neural network capable of generating both segmentation maps and probabilistic uncertainty maps. By utilizing uncertainty predictions, our method refines segmentation outcomes, achieving… More >