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Cited by 25Advanced Neural Network ApplicationsSmart Agriculture and AIInfrastructure Maintenance and Monitoring

Evaluating the Forest Ecosystem through a Semi-Autonomous Quadruped Robot and a Hexacopter UAV

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Idrissi, Moad, Hussain, Ambreen, Barua, Bidushi, Osman, Ahmed, Abozariba, Raouf, Aneiba, Adel, Asyhari, Taufiq·2022·Sensors
DOI 10.3390/s22155497PMC PMC9371004
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Abstract

Accurate and timely monitoring is imperative to the resilience of forests for economic growth and climate regulation. In the UK, forest management depends on citizen science to perform tedious and time-consuming data collection tasks. In this study, an unmanned aerial vehicle (UAV) equipped with a light sensor and positioning capabilities is deployed to perform aerial surveying and to observe a series of forest health indicators (FHIs) which are inaccessible from the ground. However, many FHIs such as burrows and deadwood can only be observed from under the tree canopy. Hence, we take the initiative of employing a quadruped robot with an integrated camera as well as an external sensing platform (ESP) equipped with light and infrared cameras, computing, communication and power modules to observe these FHIs from the ground. The forest-monitoring time can be extended by reducing computation and conserving energy. Therefore, we analysed different versions of the YOLO object-detection algorithm in terms of accuracy, deployment and usability by the EXP to accomplish an extensive low-latency detection. In addition, we constructed a series of new datasets to train the YOLOv5x and YOLOv5s for recognising FHIs. Our results reveal that YOLOv5s is lightweight and easy to train for FHI detection while performing close to real-time, cost-effective and autonomous forest monitoring.

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Authors
Idrissi, M., Hussain, A., Barua, B., Osman, A., Abozariba, R., Aneiba, A., & Asyhari, T.
Affiliations
  • Birmingham City University (GB)
  • School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK
Corresponding author
Idrissi, Moad
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2022-07-23
Volume
22
Issue
15
Pages
5497
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s22155497
PMC
PMC9371004
OpenAlex
W4287219745
Cited by
25
References
4
Citations by year
2026: 3 · 2025: 3 · 2024: 11 · 2023: 7 · 2022: 1
FWCI
1.80
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Department for Digital, Culture, Media & Sport
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Keywords
forest health indicators, forest monitoring, quadruped robot, aerial survey, YOLO, YOLOv5, YOLOv5x, lightweight detection model, citizen science, autonomous monitoring, photodetectors
MeSH terms
Robotics, Trees, Ecosystem, Remote Sensing Technology, Forests

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