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

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.

Vision System for a Forestry Navigation Machine

“…By employing computer vision and applying CNN, it becomes possible to identify and characterize the environment (trees, animals, and people) as well as the vegetation to be removed [ 11 ]. Reference [ 12 ] underscores experiments conducted in the UK aimed at monitoring forests for economic growth and climate regulation. The experiments utilized a drone equipped with camera sensors, including thermal and RGB cameras, and employed the YOLOv5 algorithm for recognition.…”

“…vegetation to be removed [11]. Reference [12] underscores experiments conducted in the UK aimed at monitoring forests for economic growth and climate regulation. The experiments utilized a drone equipped with camera sensors, including thermal and RGB cameras, and employed the YOLOv5 algorithm for recognition.…”

Using Object Detection Technology to Identify Defects in Clothing for Blind People

“…At present, and to the best knowledge of the authors, the literature still lacks a system that can automatically identify defects in clothing, an essential support tool for blind individuals to efficiently manage their wardrobe on a daily basis. Aiming at addressing this issue, a solution that utilizes a one-stage detector (YOLOv5) [38] was fine-tuned specifically for this purpose, in line with other research studies that have also efficiently employed YOLOv5 in their research [39,40]. Object detection was chosen over semantic segmentation because the presence of the defect does not require identifying details such as color, origin, type, diameter/area, or any other information that requires labeling every pixel in the image.…”

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

“…RGB cameras capture spectral information in visible light (400-700 nm), which is the same spectrum perceived by the human eye (Idrissi et al, 2022), the working principle of this kind of camera is Most common sensors used for forestry health assessment, each column represents the number of articles that used each sensor in the data collecting process.…”