MS-YOLOv5: a lightweight algorithm for strawberry ripeness detection based on deep learning
Author:
Affiliation:
1. School of Information Science and Technology, North China University of Technology, Beijing, People’s Republic of China
Funder
National Natural Science Foundation of China
R&D Program of Beijing Municipal Education Commission
Publisher
Informa UK Limited
Subject
Artificial Intelligence,Control and Optimization,Control and Systems Engineering
Link
https://www.tandfonline.com/doi/pdf/10.1080/21642583.2023.2285292
Reference31 articles.
1. Real-Time Monitoring Method of Strawberry Fruit Growth State Based on YOLO Improved Model
2. Strawberry Ripeness Identification Using Feature Extraction of RGB and K-Nearest Neighbor
3. Recognition and localization of ripen tomato based on machine vision;Arefi A.;Australian Journal of Crop Science,2011
4. Cascade R-CNN: Delving Into High Quality Object Detection
5. Research on YOLOv7-based defect detection method for automotive running lights
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