Deep learning and georeferenced RGB-D imaging for hydroponic strawberry yield mapping
Yield mapping in agricultural crops remains a significant challenge, particularly in uncontrolled environments. This study evaluates four instance segmentation algorithms: YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l, along with a low-cost GNSS RTK system to detect and count strawberries in a hydroponic e...
Saved in:
| Main Authors: | Camilo Pardo-Beainy, Carlos Parra, Leonardo Solaque, Won Suk Lee |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525005246 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Performance of Single Base RTK GNSS Method versus Network RTK
by: Nedim Onur Aykut, et al.
Published: (2015-07-01) -
D-YOLO: A Lightweight Model for Strawberry Health Detection
by: Enhui Wu, et al.
Published: (2025-03-01) -
Szybkie pozyskiwanie precyzyjnych i wiarygodnych informacji geodezyjnych w czasie rzeczywistym na potrzeby inżynierii środowiska
by: Zbigniew Siejka
Published: (2014-09-01) -
A Strawberry Ripeness Detection Method Based on Improved YOLOv8
by: Yawei Yue, et al.
Published: (2025-06-01) -
Fault Detection in Real-Time Kinematic Positioning Using Multiple Reference Stations
by: Euiho Kim, et al.
Published: (2025-07-01)