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Advanced Sensing Systems for Biological Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 1064

Special Issue Editors


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Guest Editor
Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
Interests: automated biological monitoring; biological image analysis; behavioral tracking and analysis; automated bio-monitoring systems; marine monitoring systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Pattern Processing Lab, School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan
Interests: pattern recognition; character recognition; image processing; computer vision; human–computer interaction; neurological disease analysis; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in sensing systems have significantly enhanced biological monitoring across various environments, including terrestrial, aquatic, and aerial ecosystems,  and the integration of cutting-edge technologies, such as artificial intelligence (AI), Internet of Things (IoT), machine learning, and novel sensor designs, has enabled precise, real-time data collection for diverse biological applications. These systems are crucial for tracking biodiversity, assessing ecosystem health, monitoring species distribution, and understanding environmental changes.

This Special Issue aims to highlight recent innovations and breakthroughs in advanced sensing systems for biological monitoring and we invite contributions that explore the development, implementation, and application of these technologies in various biological and ecological domains. We invite high-quality contributions on topics including, but not limited to, the following:

  • Smart and autonomous sensor networks for real-time biological monitoring;
  • AI-driven image and signal processing techniques for biological data analysis;
  • Remote and underwater sensing technologies for biodiversity assessment;
  • UAVs and satellite-based sensing for ecological monitoring;
  • Bio-inspired and bio-integrated sensors for environmental applications;
  • IoT-enabled biological monitoring systems and data fusion techniques;
  • Applications of machine learning in biological monitoring and species recognition;
  • Innovations in wearable and implantable biosensors for wildlife and human health monitoring;
  • Advanced data analytics and predictive modeling for biological system behavior;
  • Case studies and field applications of biological monitoring technologies.

We welcome researchers, engineers, and industry professionals to submit their latest findings, contributing to the future of intelligent sensing and monitoring technologies.

Dr. Chunlei Xia
Prof. Dr. Jungpil Shin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biological monitoring
  • ecological monitoring
  • biodiversity assessment
  • sensing systems

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Published Papers (1 paper)

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Research

24 pages, 5620 KB  
Article
AviaTAD-LGH: A Multi-Task Spatio-Temporal Action Detector with Lightweight Gradient Harmonization for Real-Time Avian Behavior Monitoring
by Zihui Xie, Haifang Jian, Wenhui Yang, Mengdi Fu, Wanting Peng, Markus Peter Eichhorn, Ramiro Daniel Crego, Xin Ning, Jun Du and Hongchang Wang
Sensors 2026, 26(7), 2088; https://doi.org/10.3390/s26072088 - 27 Mar 2026
Viewed by 560
Abstract
Fine-grained spatio-temporal action detection in continuous, unconstrained field videos remains a formidable challenge due to severe background clutter, high inter-class similarity, and the scarcity of domain-specific benchmarks. To address these limitations, we first introduce a large-scale Wintering-Crane Benchmark, providing dense, individual-level bounding box [...] Read more.
Fine-grained spatio-temporal action detection in continuous, unconstrained field videos remains a formidable challenge due to severe background clutter, high inter-class similarity, and the scarcity of domain-specific benchmarks. To address these limitations, we first introduce a large-scale Wintering-Crane Benchmark, providing dense, individual-level bounding box annotations for six complex behaviors across diverse habitat scenes. Leveraging this data, we propose AviaTAD-LGH, a real-time multi-task framework that incorporates auxiliary motion supervision into a dual-pathway 3D backbone to enhance feature discriminability. A critical bottleneck in such multi-task settings is the negative transfer caused by conflicting optimization objectives. To resolve this, we present Lightweight Gradient Harmonization (LGH), a plug-and-play optimization strategy that dynamically modulates task weights based on the cosine similarity of gradient directions. This mechanism effectively aligns optimization trajectories without introducing inference latency. Extensive experiments demonstrate that AviaTAD-LGH achieves a state-of-the-art mAP of 68.60%, surpassing strong public baselines by 7.44% and improving upon the single-task baseline by 2.80%, with significant gains observed on ambiguous dynamic classes. The proposed pipeline enables efficient, scalable ecological monitoring suitable for edge deployment. Full article
(This article belongs to the Special Issue Advanced Sensing Systems for Biological Monitoring)
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