Using an improved lightweight YOLOv8 model for real-time detection of multi-stage apple fruit in complex orchard environments

Using an improved lightweight YOLOv8 model for real-time detection of multi-stage apple fruit in complex orchard environments

For the purpose of monitoring apple fruits effectively throughout the entire growth period in smart orchards. A lightweight model named YOLOv8n-ShuffleNetv2-Ghost-SE was proposed. The ShuffleNetv2 basic modules and down-sampling modules were alternately connected, replacing the Backbone of YOLOv8n m...

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Bibliographic Details
Main Authors: Baoling Ma, Zhixin Hua, Yuchen Wen, Hongxing Deng, Yongjie Zhao, Liuru Pu, Huaibo Song
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2024-03-01
Series:Artificial Intelligence in Agriculture
Subjects:
Multi-stage apple fruit
Deep learning
Real-time detection
Lightweight model
Online Access:http://www.sciencedirect.com/science/article/pii/S2589721724000023

Internet

http://www.sciencedirect.com/science/article/pii/S2589721724000023

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