ASD-YOLO: a lightweight network for coffee fruit ripening detection in complex scenarios
Coffee is one of the most popular and widely used drinks worldwide. At present, how to judge the maturity of coffee fruit mainly depends on the visual inspection of human eyes, which is both time-consuming and labor-intensive. Moreover, the occlusion between leaves and fruits is also one of the chal...
Saved in:
Main Authors: | Baofeng Ye, Renzheng Xue, Haiqiang Xu |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2025-02-01
|
Series: | Frontiers in Plant Science |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1484784/full |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Light-YOLO: a lightweight detection algorithm based on multi-scale feature enhancement for infrared small ship target
by: Ji Tang, et al.
Published: (2025-01-01) -
Promoting Lampung’s coffee to the Small Medium Enterprises (SMEs) in Turkey
by: Rindu Rika Gamayuni, et al.
Published: (2024-09-01) -
Automated on-site broiler live weight estimation through YOLO-based segmentation
by: Mahmoud Y. Shams, et al.
Published: (2025-03-01) -
Design of Coffee Disease Detection Web Application Using Image Processing: A Case Study of Kyabugimbi-Kajunju Cooperative Farms in Bushenyi District.
by: Asiimwe, Mark, et al.
Published: (2024) -
Technological Factors And coffee Consumption In Rukungiri District, Southwestern Uganda.
by: Turyasingura, Moses, et al.
Published: (2023)