A Novel Air-to-Ground Communication Scheme for Advanced Big Data Collection in Smart Farming Using UAVs by Georgios A. Kakamoukas, Thomas D. Lagkas, Vasileios Argyriou, Sotirios K. Goudos, Panagiotis Radoglou-Grammatikis, Stamatia Bibi, Panagiotis G. Sarigiannidis

“…The proposed system replaces the single UAV approach with a cooperative FANET comprising multiple UAVs, significantly reducing mission completion time while facilitating real-time data transmission through the cooperation of aerial and ground nodes. …”
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SR-YOLO: Spatial-to-Depth Enhanced Multi-Scale Attention Network for Small Target Detection in UAV Aerial Imagery by Shasha Zhao, He Chen, Di Zhang, Yiyao Tao, Xiangnan Feng, Dengyin Zhang

“…It is specifically tailored to address these challenges in UAV-captured aerial images. First, the Space-to-Depth layer and Receptive Field Attention Convolution are combined, and the SR-Conv module is designed to replace the Conv module within the original backbone network. …”
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Multispectral Images for Drought Stress Evaluation of Arabica Coffee Genotypes Under Different Irrigation Regimes by Patrícia Carvalho da Silva, Walter Quadros Ribeiro Junior, Maria Lucrecia Gerosa Ramos, Maurício Ferreira Lopes, Charles Cardoso Santana, Raphael Augusto das Chagas Noqueli Casari, Lemerson de Oliveira Brasileiro, Adriano Delly Veiga, Omar Cruz Rocha, Juaci Vitória Malaquias, Nara Oliveira Silva Souza, Henrique Llacer Roig

“…The advancement of digital agriculture combined with computational tools and Unmanned Aerial Vehicles (UAVs) has opened the way to large-scale data collection for the calculation of vegetation indices (VIs). …”
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SPDC-YOLO: An Efficient Small Target Detection Network Based on Improved YOLOv8 for Drone Aerial Image by Jingxin Bi, Keda Li, Xiangyue Zheng, Gang Zhang, Tao Lei

“…Target detection in UAV images is of great significance in fields such as traffic safety, emergency rescue, and environmental monitoring. …”
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YOLO-SMUG: An Efficient and Lightweight Infrared Object Detection Model for Unmanned Aerial Vehicles by Xinzhe Luo, Xiaogang Zhu

“…To tackle the high computational demands and accuracy limitations in UAV-based infrared object detection, this study proposes YOLO-SMUG, a lightweight detection algorithm optimized for small object identification. …”
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MEP-YOLOv5s: Small-Target Detection Model for Unmanned Aerial Vehicle-Captured Images by Shengbang Zhou, Song Zhang, Chuanqi Li, Shutian Liu, Dong Chen

“…This article introduces a drone detection model, MEP-YOLOv5s, which optimizes the Backbone, Neck layer, and C3 module based on YOLOv5s, and combines effective attention mechanisms to improve the training efficiency of the model by replacing the traditional CIoU loss (Complete Intersection over Union) with MPDIoU (Minimum Point Distance-based Intersection over Union) loss. …”
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A novel lightweight YOLOv8-PSS model for obstacle detection on the path of unmanned agricultural vehicles by Zhijian Chen, Yijun Fang, Jianjun Yin, Shiyu Lv, Farhan Sheikh Muhammad, Lu Liu

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Improved YOLOv8 Object Detection Method for Drone Aerial Images by Zhong Shuai, Wang Liping

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Vehicle detection in drone aerial views based on lightweight OSD-YOLOv10 by Yang Zhang, Xiaobing Chen, Su Sun, Hongfeng You, Yuanyuan Wang, Jianchu Lin, Jiacheng Wang

“…Abstract To address the challenges of low performance in vehicle image detection from UAV aerial imagery, difficulties in small target feature extraction, and the large parameter size of existing models, we propose the OSD-YOLOv10 algorithm, an enhanced version based on YOLOv10n. …”
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Incremental Cotton Diseases Detection Based on Aerial Remote Sensing Image and Blockchain Sharding by Jing Nie, Haochen Li, Yang Li, Jingbin Li, Xuewei Chao, Sezai Ercisli

“…In addition, the blockchain is further partitioned and a reputation evaluation mechanism is added to the process of federated learning model aggregation to optimize the whole federated learning process. Finally, pest and disease images were collected from cotton fields in several surrounding areas by UAV to construct a dataset on which distributed federation learning was trained. …”
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YOLO-SRMX: A Lightweight Model for Real-Time Object Detection on Unmanned Aerial Vehicles by Shimin Weng, Han Wang, Jiashu Wang, Changming Xu, Ende Zhang

“…Unmanned Aerial Vehicles (UAVs) face a significant challenge in balancing high accuracy and high efficiency when performing real-time object detection tasks, especially amidst intricate backgrounds, diverse target scales, and stringent onboard computational resource constraints. …”
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Assignment Technology Based on Improved Great Wall Construction Algorithm by Xianjun Zeng, Yao Wei, Yang Yu, Hanjie Hu, Qixiang Tang, Ning Hu

“…The problem of allocating multiple UAV tasks is a complex combinatorial optimization challenge, involving various constraints. …”
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Efficient vision transformers with edge enhancement for robust small target detection in drone-based remote sensing by Xuguang Zhu, Zhizhao Zhang

“…Combined with an Inner Generalized Intersection-over-Union (Inner-GIoU) loss function to optimize bounding box geometric consistency, MLD-DETR achieves 36.7% AP50% and 14.5% APs on the VisDrone2019 dataset, outperforming the baseline RT-DETR by 3.2% and 1.8% in accuracy while achieving 20% parameter reduction and maintaining computational efficiency suitable for UAV platforms equipped with modern edge computing hardware. …”
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RTAPM: A Robust Top-View Absolute Positioning Method with Visual–Inertial Assisted Joint Optimization by Pengfei Tong, Xuerong Yang, Xuanzhi Peng, Longfei Wang

“…Currently, there is no unmanned aerial vehicle (UAV) positioning method that is capable of substituting or temporarily replacing GNSS positioning. …”
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A Novel Robust Position Integration Optimization-Based Alignment Method for In-Flight Coarse Alignment by Xiaoge Ning, Jixun Huang, Jianxun Li

“…In-flight alignment is a critical milestone for inertial navigation system/global navigation satellite system (INS/GNSS) applications in unmanned aerial vehicles (UAVs). The traditional position integration formula for in-flight coarse alignment requires the GNSS velocity data to be valid throughout the alignment period, which greatly limits the engineering applicability of the method. …”
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Hyperspectral Anomaly Detection by Spatial–Spectral Fusion Based on Extreme Value-Entropy Band Selection and Cauchy Graph Distance Optimization by Song Zhao, Yali Lv, Wen Zhang, Lijun Wang, Zhiru Yang, Gaofeng Ren, Bin Wang, Xiaobin Zhao, Tongwei Lu, Jiayao Wang, Wei Li

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Formation control method for multiple flight vehicles based on adaptive dynamic programming by Jie XIAO, Qingpu TANG, Jiale LI, Guofei LI

“…Reinforcement learning theory has emerged as a powerful approach to solving optimal control problems in nonlinear systems. The theory’s ability to learn and adapt to dynamic environments makes it particularly well-suited for UAV control. …”
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METHODOLOGY AND RESULTS OF THE MAIN TECHNICAL OF PARAMETERS OF THE MANEUVERABLE UNMANNED AERIAL VEHICLE JUSTIFICATION by M. A. Kiselev

“…The greatest challenge lies in creating the algorithms, data sensors, control hardware, communications hardware, etc. necessary for utilization of an unmanned aerial vehicle (UAV). In this context it is important to highlight the problem of replacing the pilot as a sensor and a flight operator on board of the UAV. …”
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FRPNet: A Lightweight Multi-Altitude Field Rice Panicle Detection and Counting Network Based on Unmanned Aerial Vehicle Images by Yuheng Guo, Wei Zhan, Zhiliang Zhang, Yu Zhang, Hongshen Guo

“…This paper proposes FRPNet, a novel lightweight convolutional neural network optimized for multi-altitude rice panicle detection in UAV images. …”
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Large-scale tobacco identification via a very-high-resolution unmanned aerial vehicle benchmark and a ConvFlow Transformer by Wei Han, Shaohao Chen, Shuanglin Xiao, Yunliang Chen, Huihui Zhao, Jining Yan, Xiaohan Zhang, Sheng Wang

“…Moreover, to the best of our knowledge, no tobacco dataset is accessible to the public, impeding the development of a deep learning (DL) model with optimal performance. Therefore, a Large-scale UAV remote SEnsing Tobacco dataset (LUSET) which is the world’s first tobacco dataset with a total volume of 67GB has been conducted in this paper. 10 large-scale images in the LUSET are accurately annotated with an average resolution of about 20,000 × 20,000 pixels, which can be divided into 7,252 512 × 512 samples11 https://github.com/Monkeycrop/UAV-Tobacco-Dataset.. …”
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