Citation: Moad, I.; Hussain, A.;
Barua, B.; Osman, A.; Abozariba, R.;
Aneiba, A.; Asyhari, T. Evaluating the
Forest Ecosystem through a Semi-
Autonomous Quadruped Robot and
a Hexacopter UAV. Sensors 2022, 22,
5497. https://doi.org/10.3390/
s22155497
Academic Editors: Yangquan Chen,
Nunzio Cennamo, M. Jamal Deen,
Subhas Mukhopadhyay, Simone
Morais and Junseop Lee
Received: 6 June 2022
Accepted: 19 July 2022
Published: 23 July 2022
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Article
Evaluating the Forest Ecosystem through a Semi-Autonomous
Quadruped Robot and a Hexacopter UAV
Moad Idrissi * , Ambreen Hussain, Bidushi Barua, Ahmed Osman, Raouf Abozariba , Adel Aneiba
and Taufiq Asyhari
School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK;
ambreen.hussain@ieee.org (A.H.); bidushi.barua@bcu.ac.uk (B.B.); ahmed.osman@mail.bcu.ac.uk (A.O.);
raouf.abozariba@bcu.ac.uk (R.A.); adel.aneiba@bcu.ac.uk (A.A.); taufiq-a@ieee.org (T.A.)
* Correspondence: moad.idrissi@bcu.ac.uk
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.
Keywords: forest health indicators; object detection; YOLOv5; WebRTC; 5G; real-time monitoring
1. Introduction
Forests are an integral part of our environment and hold an essential capacity for the
survival of human beings, animals and life in general. Forests and woodlands represent
approximately 13% of the total land area in the UK. They provide habitats for myriad
animals, insects, etc., and help maintain the balance in the atmosphere. These forest
woodland resources are highly valued for a wide range of services, including timber
production, water, air-quality improvement, biodiversity and several aesthetic and health
benefits for humanity [
1
]. The monetary value of these UK forests was estimated to be as
much as £130 billion [
2
] as in 2017 and continues to grow. The forests also have a crucial
role in combating climate change threats by contributing to urban cooling, mitigation
of floods and carbon sequestration. Due to this, it has become necessary to maintain
and restore trees and forests through different activities such as seed dispersal, planting
trees and preventing trees from falling. In 2019, the program of creating 13,700 Ha of
more woodland was initiated in the UK [
3
], which will be accentuated in the years ahead.
Another initiative undertaken in this regard is by the UK’s Woodland Trust, which proposed
to plant
50 million
native trees over a period of 25 years. Apart from the creation of new
forests and conducting surveys, it is necessary to take relevant actions for the conservation
work to save the declining species and habitats [
4
]. Therefore, actions must be taken to
protect, conserve and enhance wildlife and fish habitats, and protect other ecosystems,
Sensors 2022, 22, 5497. https://doi.org/10.3390/s22155497 https://www.mdpi.com/journal/sensors
