Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections.

Saved in:
Bibliographic Details
Title: Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections.
Authors: Ashurov AY; School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China., Al-Gaashani MSAM; School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, China., Samee NA; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia., Alkanhel R; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia., Atteia G; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia., Abdallah HA; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia., Saleh Ali Muthanna M; Department of International Business Management, Tashkent State University of Economics, Tashkent, Uzbekistan.
Source: Frontiers in plant science [Front Plant Sci] 2025 Jan 23; Vol. 15, pp. 1505857. Date of Electronic Publication: 2025 Jan 23 (Print Publication: 2024).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101568200 Publication Model: eCollection Cited Medium: Print ISSN: 1664-462X (Print) Linking ISSN: 1664462X NLM ISO Abbreviation: Front Plant Sci Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1664-462X
DOI:10.3389/fpls.2024.1505857