DGS-Yolov7-Tiny: a lightweight pest and disease target detection model suitable for edge computing environments

DGS-Yolov7-Tiny: a lightweight pest and disease target detection model suitable for edge computing environments

Abstract Pest detection is vital for maintaining crop health in modern agriculture. However, traditional object detection models are often computationally intensive and complex, rendering them unsuitable for real-time applications in edge computing. To overcome this limitation, we proposed DGS-YOLOv...

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Bibliographic Details
Main Authors: Ping Yu, Baoshu Zong, Xiaozhong Geng, Hui Yan, Baijin Liu, Cheng Chen, Hupeng Liu, Xiaoqing Xu
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Lightweight model
Depthwise separable convolution (DSC)
Object detection
Mobile edge computing (MEC)
Artificial Intelligence(AI)
Online Access:https://doi.org/10.1038/s41598-025-13410-8
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https://doi.org/10.1038/s41598-025-13410-8

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