• Forest fire prediction using i...

*Result*: Forest fire prediction using image processing.

Title:
Forest fire prediction using image processing.
Authors:
Li Y; School of Electronic Information Engineering, Guiyang University, Guiyang, China.; School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China., Chen J; School of Electronic Information Engineering, Guiyang University, Guiyang, China., Zeng Y; School of Electronic Information Engineering, Guiyang University, Guiyang, China., Ding Y; School of Electronic Information Engineering, Guiyang University, Guiyang, China., Huang C; Fire Brigade, Ziyun County, Anshun, China., Tian H; School of Electronic Information Engineering, Guiyang University, Guiyang, China.
Source:
PloS one [PLoS One] 2026 Jan 20; Vol. 21 (1), pp. e0338794. Date of Electronic Publication: 2026 Jan 20 (Print Publication: 2026).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Imprint Name(s):
Original Publication: San Francisco, CA : Public Library of Science
MeSH Terms:
Wildfires*/prevention & control , Image Processing, Computer-Assisted*/methods , Forests* , Fires*, Humans ; Algorithms ; Models, Theoretical
References:
Trends Cogn Sci. 2019 Apr;23(4):305-317. (PMID: 30795896)
IEEE Trans Pattern Anal Mach Intell. 2023 Jan;45(1):87-110. (PMID: 35180075)
Heliyon. 2023 Dec 02;10(1):e23127. (PMID: 38163175)
Entry Date(s):
Date Created: 20260120 Date Completed: 20260120 Latest Revision: 20260123
Update Code:
20260130
PubMed Central ID:
PMC12818664
DOI:
10.1371/journal.pone.0338794
PMID:
41557623
Database:
MEDLINE

*Further Information*

*Forest fires pose a significant threat to public safety and the environment, and harmful pollutants spread rapidly in areas covered by vegetation. Early detection is very important for preventing forest fires from evolving into catastrophic fires. The traditional prediction methods have relatively low accuracy. They can only identify fires clearly after they occur, making it difficult to meet the requirements of precise real-time detection. The YOLOv5-PSG model proposed in this paper improves the YOLOv5 model. After 300 rounds of training, the average recognition accuracy rate of mAP can reach 93.1%, and the accuracy rate can reach approximately 0.802. After 300 rounds of training and learning, the confidence level can reach about 0.965. This improvement makes fire early warning and prediction more comprehensive and effective, ultimately protecting human life and the environment by mitigating the impact of wildfires.
(Copyright: © 2026 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)*

*The authors have declared that no competing interests exist.*