Significance of AI-assisted techniques for epiphyte plant monitoring and identification from drone images.

Autor: V V SV; Amrita School of Artificial Intelligence, Coimbatore, Amrita Vishwa Vidyapeetham, 641112, India. Electronic address: vv_sajithvariyar@cb.amrita.edu., V S; Amrita School of Artificial Intelligence, Coimbatore, Amrita Vishwa Vidyapeetham, 641112, India. Electronic address: v_sowmya@cb.amrita.edu., Sivanpillai R; Wyoming GIS Center, School of Computing, University of Wyoming, Laramie, WY, 82071, USA. Electronic address: sivan@uwyo.edu., Brown GK; Department of Botany, University of Wyoming, Laramie, WY, 82071, USA. Electronic address: gkbrown@uwyo.edu.
Jazyk: angličtina
Zdroj: Journal of environmental management [J Environ Manage] 2024 Sep; Vol. 367, pp. 121996. Date of Electronic Publication: 2024 Jul 31.
DOI: 10.1016/j.jenvman.2024.121996
Abstrakt: Monitoring forest canopies is vital for ecological studies, particularly for assessing epiphytes in rain forest ecosystems. Traditional methods for studying epiphytes, such as climbing trees and building observation structures, are labor, cost intensive and risky. Unmanned Aerial Vehicles (UAVs) have emerged as a valuable tool in this domain, offering botanists a safer and more cost-effective means to collect data. This study leverages AI-assisted techniques to enhance the identification and mapping of epiphytes using UAV imagery. The primary objective of this research is to evaluate the effectiveness of AI-assisted methods compared to traditional approaches in segmenting/identifying epiphytes from UAV images collected in a reserve forest in Costa Rica. Specifically, the study investigates whether Deep Learning (DL) models can accurately identify epiphytes during complex backgrounds, even with a limited dataset of varying image quality. Systematically, this study compares three traditional image segmentation methods Auto Cluster, Watershed, and Level Set with two DL-based segmentation networks: the UNet and the Vision Transformer-based TransUNet. Results obtained from this study indicate that traditional methods struggle with the complexity of vegetation backgrounds and variability in target characteristics. Epiphyte identification results were quantitatively evaluated using the Jaccard score. Among traditional methods, Watershed scored 0.10, Auto Cluster 0.13, and Level Set failed to identify the target. In contrast, AI-assisted models performed better, with UNet scoring 0.60 and TransUNet 0.65. These results highlight the potential of DL approaches to improve the accuracy and efficiency of epiphyte identification and mapping, advancing ecological research and conservation.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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