Object Detection and Control of Networked Autonomous Systems: Theories, Analysis Tools and Applications

Topic Information

Dear Colleagues,

Networked autonomous systems for object detection and control are rapidly transforming applications in agriculture, food systems, forestry, plant sciences, electrification, civil engineering, and beyond. Leveraging advances in computer vision, satellite and high-altitude remote sensing, unmanned aerial/surface/ground vehicles, robotics, and sensor networks, these platforms fuse data from satellites, drones, ground robots, and fixed sensors to deliver precise, real-time detection, state estimation, and decision making across production, processing, and distribution chains. Such systems support tasks including crop and forest health monitoring, post-harvest quality assessment and food safety monitoring, invasive species localization, infrastructure inspection, power grid surveillance, and ecosystem management. Integrated sensing and AI-driven control enable real-time quality assurance and rapid response across the food supply chain. Recent breakthroughs in deep learning, edge computing, energy-harvesting sensor design, and wireless communications have greatly enhanced autonomy, reliability, safety, and scalability. With sustainable resource management and resilient infrastructure growing in importance, as well as increasing attention being paid to food security and supply-chain resilience, there is an urgent need for new theoretical frameworks, control algorithms, hardware–software co-design methods, and field-validated case studies that advance object detection and control in networked autonomous systems from theory to practice. This topic invites original contributions on emerging concepts, design and analysis tools, and innovative application cases, including, but not limited to, the following:

• Multi-platform object detection and data fusion algorithms.
• Vision-based perception and object tracking with UAVs and ground robots.
• Satellite and high-altitude remote sensing methods for precise object recognition and localization.
• Energy-autonomous sensor and actuator network design.
• Deep learning for real-time object recognition and control decision making.
• Cooperative task planning and control among aerial, surface, and ground vehicles.
• Robotics and automation for precision operations (weeding, seeding, harvesting, and post-harvest sorting and packaging).
• Thermal–infrared and multispectral imaging for state estimation and environmental modeling.
• Digital twin development for farmland and forest ecosystem management and control.
• Autonomous navigation and obstacle avoidance in complex terrains.
• Fault-tolerant power electronics and energy management for unmanned systems.
• Formal verification and safety certification of large-scale networked systems.
• Data-driven modeling and control of smart irrigation and fertilization systems.
• Remote robotic inspection and rehabilitation of aging infrastructure.
• Integration of LiDAR, RADAR, and photogrammetry for 3D environmental perception and control.
• Networked sensing and control for food science, engineering, and production.Case studies on scalable deployment in agro-forestry, food supply chains, botanical research, and engineering projects.
• Active disturbance rejection control in motor and generator control systems.
• Optimization control of hybrid propulsion systems of aircraft, ships, and vehicles.
• Mechanical vibration and noise control, friction nanogeneration technology, marine machinery condition monitoring, and fault diagnosis technology.
• Data-driven modeling and distributed filtering for autonomous systems.
• Resilient data-driven distributed filtering against complex cyberattacks.
• Distributed multi-objective task allocation for autonomous systems.
• Networked sensing and control for food processing and production—monitoring chemical and physical properties, real-time quality grading, and automated sorting.
• Distributed detection and control for food safety and supply-chain resilience—microbial/contaminant surveillance, cold-chain and grain-storage monitoring, and traceability.
• Distributed detection and control for food safety, quality assurance, and supply-chain resilience.
• Networked autonomous detection and control for food production and processing.

Prof. Dr. Jian Li
Prof. Dr. Xu Fang
Dr. Jiaqi Yan
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.6	6.3	2011	18 Days	CHF 2600	Submit
Plants plants	4.1	7.6	2012	17.7 Days	CHF 2700	Submit
Robotics robotics	3.3	7.7	2012	21.8 Days	CHF 1800	Submit
Foods foods	5.1	8.7	2012	14.9 Days	CHF 2900	Submit
Horticulturae horticulturae	3.0	5.1	2015	17.1 Days	CHF 2200	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (2 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Agriculture Plants Robotics Foods Horticulturae Electronics

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

26 pages, 5890 KB  

Open AccessArticle

Research on Accurate Weed Identification and a Variable Application Method in Maize Fields Based on an Improved YOLOv11n Model

by Xiaoan Chen, Hongze Zhang, Xingcheng Liu, Zhonghui Guo, Wei Zheng and Yingli Cao

Agriculture 2025, 15(23), 2456; https://doi.org/10.3390/agriculture15232456 - 27 Nov 2025

Viewed by 152

Abstract

Uniform spraying by conventional plant protection drones often results in low herbicide utilization efficiency and environmental contamination, both of which are critical issues in agricultural production. To address these challenges, this study proposed a precision weed management system for maize fields that combines [...] Read more.

Uniform spraying by conventional plant protection drones often results in low herbicide utilization efficiency and environmental contamination, both of which are critical issues in agricultural production. To address these challenges, this study proposed a precision weed management system for maize fields that combines an improved YOLOv11n-OSAW detection model with DJI drones for variable-rate herbicide application. The YOLOv11n-OSAW model was enhanced with Omni-dimensional Dynamic Convolution (OD-Conv), the SEAM attention mechanism, a lightweight ADown module, and the Wise-IoU (WIoU) loss function, aiming to improve the detection accuracy of small and occluded weeds in maize fields. When the model was deployed on an uncrewed aerial vehicle (UAV) operating at 5 m altitude, it achieved mean Average Precision mAP@0.5 values of 97.8% and 97.0% for gramineous and broad-leaved weeds, respectively—representing increases of 2.9 and 1.6 percentage points over the baseline YOLOv11n model. Weed distribution maps generated from the detection results were used to develop site-specific herbicide prescription maps, guiding the drone to implement targeted spraying. Water-sensitive paper analysis verified that the system ensured effective droplet deposition and uniform coverage across different application rate areas. This integrated workflow, covering UAV image acquisition, weed detection, variable-rate application, and effect assessment, reduced herbicide consumption by 20.25% compared with conventional uniform spraying (450 L/ha) while maintaining excellent weed control efficiency and reducing environmental risks. The findings demonstrate that the proposed system provides a practical and sustainable solution for weed management in maize fields. Full article

(This article belongs to the Topic Object Detection and Control of Networked Autonomous Systems: Theories, Analysis Tools and Applications)

►▼ Show Figures

Figure 1

17 pages, 10904 KB  

Open AccessArticle

Self-Supervised Infrared Image Denoising via Adaptive Gradient-Perception Network for FPN Suppression

by Yue Tang, Chaobo Min, Runzhe Miao and Jiajia Lu

Electronics 2025, 14(21), 4334; https://doi.org/10.3390/electronics14214334 - 5 Nov 2025

Viewed by 404

Abstract

Current denoising algorithms in infrared imaging systems predominantly target either high-frequency stripe noise or Gaussian noise independently, failing to adequately address the prevalent hybrid noise in real-world scenarios. To tackle this challenge, we propose a convolutional neural network (CNN)-based approach with a refined [...] Read more.

Current denoising algorithms in infrared imaging systems predominantly target either high-frequency stripe noise or Gaussian noise independently, failing to adequately address the prevalent hybrid noise in real-world scenarios. To tackle this challenge, we propose a convolutional neural network (CNN)-based approach with a refined composite loss function, specifically designed for hybrid noise removal in raw infrared images. Our method employs a residual network backbone integrated with an adaptive weighting mechanism and edge-preserving loss, enabling joint modeling of multiple noise types while safeguarding structural edges. Unlike reference-based CNN denoising methods requiring clean images, our solution leverages intrinsic gradient variations within image sequences for adaptive smoothing, eliminating dependency on ground-truth data during training. Rigorous experiments conducted on three public datasets have demonstrated the optimal or suboptimal performance of our method in mixed noise suppression and detail preservation (PSNR > 32.13/SSIM > 0.8363). Full article

(This article belongs to the Topic Object Detection and Control of Networked Autonomous Systems: Theories, Analysis Tools and Applications)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-2