YOLO-Based Tree Trunk Types Multispectral Perception: A Two-Genus Study at Stand-Level for Forestry Inventory Management Purposes

• Type: Article in International Scientific Journal
• Date: 2024

Authors

da Silva, DQ (Author) Other; Filipe Neves Santos (Author) FEUP; Filipe, V (Author) Other; Armando Jorge Sousa (Author) FEUP; Pires, EJS (Author) Other

Journal

Title: IEEE Access

Vol. 12

ISSN: 2169-3536

Publisher: IEEE

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-016-RPA

DOI: 10.1109/access.2024.3443528

Abstract (EN): Stand-level forest tree species perception and identification are needed for monitoring-related operations, being crucial for better biodiversity and inventory management in forested areas. This paper contributes to this knowledge domain by researching tree trunk types multispectral perception at stand-level. YOLOv5 and YOLOv8 - Convolutional Neural Networks specialized at object detection and segmentation - were trained to detect and segment two tree trunk genus (pine and eucalyptus) using datasets collected in a forest region in Portugal. The dataset comprises only two categories, which correspond to the two tree genus. The datasets were manually annotated for object detection and segmentation with RGB and RGB-NIR images, and are publicly available. The Small variant of YOLOv8 was the best model at detection and segmentation tasks, achieving an F1 measure above 87% and 62%, respectively. The findings of this study suggest that the use of extended spectra, including Visible and Near Infrared, produces superior results. The trained models can be integrated into forest tractors and robots to monitor forest genus across different spectra. This can assist forest managers in controlling their forest stands.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13