Izvorni znanstveni članak
https://doi.org/10.5552/crojfe.2023.2012
Tree Trunk Detection of Eastern Red Cedar in Rangeland Environment with Deep Learning Technique
Chetan Badgujar
orcid.org/0000-0002-7698-2843
; Kansas State University Biological and Agricultural Engineering Seaton 1037A, 920 N. Martin Luther King Jr. Drive Manhattan, KS 66506 USA
*
Daniel Flippo
; Kansas State University Biological and Agricultural Engineering Seaton Hall 1033, 920 N. Martin Luther King Jr. Drive Manhattan, KS 66506 USA
Sujith Gunturu
; Kansas State University Computer Science Seaton 1037A, 920 N. Martin Luther King Jr. Drive Manhattan, KS 66506 USA
Carolyn Baldwin
; Kansas State University Agriculture, Natural Resources and Community Vitality Umberger 103, 1612 Claflin Manhattan, Kansas 66506 USA
* Dopisni autor.
Sažetak
Uncontrolled spread of eastern red cedar invades the United States Great Plains prairie ecosystems and lowers biodiversity across native grasslands. The eastern red cedar (ERC) infestations cause significant challenges for ranchers and landowners, including the high costs of removing mature red cedars, reduced livestock forage feed, and reduced revenue from hunting leases. Therefore, a fleet of autonomous ground vehicles (AGV) is proposed to address the ERC infestation. However, detecting the target tree or trunk in a rangeland environment is critical in automating an ERC cutting operation. A tree trunk detection method was developed in this study for ERC trees trained in natural rangeland environments using a deep learning-based YOLOv5 model. An action camera acquired RGB images in a natural rangeland environment. A transfer learning method was adopted, and the YOLOv5 was trained to detect the varying size of the ERC tree trunk. A trained model precision, recall, and average precision were 87.8%, 84.3%, and 88.9%. The model accurately predicted the varying tree trunk sizes and differentiated between trunk and branches. This study demonstrated the potential for using pretrained deep learning models for tree trunk detection with RGB images. The developed machine vision system could be effectively integrated with a fleet of AGVs for ERC cutting. The proposed ERC tree trunk detection models would serve as a fundamental element for the AGV fleet, which would assist in effective rangeland management to maintain the ecological balance of grassland systems.
Ključne riječi
Hrčak ID:
310152
URI
Datum izdavanja:
14.7.2023.
Posjeta: 498 *