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

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

This study proposes an advanced method for plant disease detection utilizing a modified depthwise convolutional neural network (CNN) integrated with squeeze-and-excitation (SE) blocks and improved residual skip connections. In light of increasing global challenges related to food security and sustai...

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Bibliographic Details
Main Authors: Asadulla Y. Ashurov, Mehdhar S. A. M. Al-Gaashani, Nagwan A. Samee, Reem Alkanhel, Ghada Atteia, Hanaa A. Abdallah, Mohammed Saleh Ali Muthanna
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Plant Science
Subjects:
deep learning
plant disease detection
convolutional neural network
squeeze and excitation (SE) blocks
residual skip connection
Online Access:https://www.frontiersin.org/articles/10.3389/fpls.2024.1505857/full
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