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

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