Multi-class fruit ripeness detection using YOLO and SSD object detection models

Multi-class fruit ripeness detection using YOLO and SSD object detection models

Abstract Accurate fruit ripeness detection is critical to reducing post-harvest losses and improving quality control in agricultural systems. This study benchmarks four object detection models—YOLOv5, YOLOv6, YOLOv7, and SSD-MobileNetv1—for multi-class ripeness classification of strawberries and avo...

Full description

Saved in:
Bibliographic Details
Main Authors: Pooja Kamat, Shilpa Gite, Harsh Chandekar, Lisanne Dlima, Biswajeet Pradhan
Format: Article
Language:English
Published: Springer 2025-08-01
Series:Discover Applied Sciences
Subjects:
Computer vision
Object detection
YOLO
Fruit ripeness detection
SSD-MobileNet
Deep learning
Online Access:https://doi.org/10.1007/s42452-025-07617-7
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1007/s42452-025-07617-7

Similar Items