High-Definition Garden Plant Images Threshold Segmentation Mechanism Based on PSO and DRL

Shi Ji, Tianlu Xi and Xingchen Fan
Additional contact information
Shi Ji: Taizhou University, China
Tianlu Xi: LuXun Academy of Fine Arts, China
Xingchen Fan: Jeonju University, South Korea

International Journal of Swarm Intelligence Research (IJSIR), 2024, vol. 15, issue 1, 1-17

Abstract: The accuracy of the threshold determines the quality of high-definition garden plant image segmentation. How to accurately and quickly search for the best combination of multiple thresholds is currently a research difficulty. In this regard, this article proposes an improved adaptive particle swarm optimization algorithm with extremal disturbance (IAPSO), which can to some extent prevent the PSO from falling into local optima by implementing extreme perturbation strategies. Then, by combining IAPSO and Deep Reinforcement Learning (DRL), the IAPSO-RL based on policy gradient off policy is proposed. It enhances information exchange between DRL and PSO. The IAPSO-RL can improve the sample efficiency of PSO. Experiments have shown that it can improve the performance and stability of threshold segmentation for high-definition garden plant images.

Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSIR.348970 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jsir00:v:15:y:2024:i:1:p:1-17

Access Statistics for this article

International Journal of Swarm Intelligence Research (IJSIR) is currently edited by Yuhui Shi

More articles in International Journal of Swarm Intelligence Research (IJSIR) from IGI Global
Bibliographic data for series maintained by Journal Editor ().