气候变化领域集成服务门户(CCPortal): A modified generative adversarial networks with Yolov5 for automated forest health diagnosis from aerial imagery and Tabu search algorithm

CCPortal

DOI	10.1038/s41598-024-54399-w
	A modified generative adversarial networks with Yolov5 for automated forest health diagnosis from aerial imagery and Tabu search algorithm
	Jayagopal, Prabhu; Janaki, Kumar Purushothaman; Mohan, Prakash; Kondapaneni, Upendra Babu; Periyasamy, Jayalakshmi; Mathivanan, Sandeep Kumar; Dalu, Gemmachis Teshite
发表日期	2024
ISSN	2045-2322
起始页码	14
结束页码	1
卷号	14 期号:1
英文摘要	Our environment has been significantly impacted by climate change. According to previous research, insect catastrophes induced by global climate change killed many trees, inevitably contributing to forest fires. The condition of the forest is an essential indicator of forest fires. Analysis of aerial images of a forest can detect deceased and living trees at an early stage. Automated forest health diagnostics are crucial for monitoring and preserving forest ecosystem health. Combining Modified Generative Adversarial Networks (MGANs) and YOLOv5 (You Only Look Once version 5) is presented in this paper as a novel method for assessing forest health using aerial images. We also employ the Tabu Search Algorithm (TSA) to enhance the process of identifying and categorizing unhealthy forest areas. The proposed model provides synthetic data to supplement the limited labeled dataset, thereby resolving the frequent issue of data scarcity in forest health diagnosis tasks. This improvement enhances the model's ability to generalize to previously unobserved data, thereby increasing the overall precision and robustness of the forest health evaluation. In addition, YOLOv5 integration enables real-time object identification, enabling the model to recognize and pinpoint numerous tree species and potential health issues with exceptional speed and accuracy. The efficient architecture of YOLOv5 enables it to be deployed on devices with limited resources, enabling forest-monitoring applications on-site. We use the TSA to enhance the identification of unhealthy forest areas. The TSA method effectively investigates the search space, ensuring the model converges to a near-optimal solution, improving disease detection precision and decreasing false positives. We evaluated our MGAN-YOLOv5 method using a large dataset of aerial images of diverse forest habitats. The experimental results demonstrated impressive performance in diagnosing forest health automatically, achieving a detection precision of 98.66%, recall of 99.99%, F1 score of 97.77%, accuracy of 99.99%, response time of 3.543 ms and computational time of 5.987 ms. Significantly, our method outperforms all the compared target detection methods showcasing a minimum improvement of 2% in mAP.
英文关键词	Forest fires; Modified generative adversarial networks; YOLOv5; Tabu search algorithm; Deep learning; Aerial imagery
语种	英语
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:001177603900016
来源期刊	SCIENTIFIC REPORTS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/291570
作者单位	Vellore Institute of Technology (VIT); VIT Vellore; Vellore Institute of Technology (VIT); VIT Vellore; Bharath Institute of Higher Education & Research; Galgotias University; Haramaya University
推荐引用方式 GB/T 7714	Jayagopal, Prabhu,Janaki, Kumar Purushothaman,Mohan, Prakash,et al. A modified generative adversarial networks with Yolov5 for automated forest health diagnosis from aerial imagery and Tabu search algorithm[J],2024,14(1).
APA	Jayagopal, Prabhu.,Janaki, Kumar Purushothaman.,Mohan, Prakash.,Kondapaneni, Upendra Babu.,Periyasamy, Jayalakshmi.,...&Dalu, Gemmachis Teshite.(2024).A modified generative adversarial networks with Yolov5 for automated forest health diagnosis from aerial imagery and Tabu search algorithm.SCIENTIFIC REPORTS,14(1).
MLA	Jayagopal, Prabhu,et al."A modified generative adversarial networks with Yolov5 for automated forest health diagnosis from aerial imagery and Tabu search algorithm".SCIENTIFIC REPORTS 14.1(2024).