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Vase-Life Monitoring System for Cut Flowers Using Deep Learning and Multiple Cameras.

Ji Yeong Ham¹, Yong-Tae Kim¹, Suong Tuyet Thi Ha¹

¹Department of Smart Horticultural Science, Andong National University, Andong 36729, Republic of Korea.

Plants (Basel, Switzerland)

|April 12, 2025

Summary

This summary is machine-generated.

A novel vase-life monitoring system (VMS) uses AI and imaging to accurately assess cut rose quality and vase life. This system precisely tracks physiological changes and disease, aiding breeders in developing superior cultivars.

Keywords:

artificial intelligence deep learning microscope monitoring rose vase life

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Area of Science:

Horticulture
Plant Physiology
Artificial Intelligence

Background:

Assessing post-harvest quality and vase life (VL) of cut flowers is crucial for the floriculture industry.
Current methods for monitoring cut rose physiology and disease are often manual and time-consuming.

Purpose of the Study:

To develop an automated system for accurate and continuous assessment of cut rose post-harvest quality and VL.
To integrate deep learning for enhanced physiological parameter monitoring and disease detection.

Main Methods:

Development of a vase-life monitoring system (VMS) integrating camera imaging and the YOLOv8 deep learning algorithm.
Continuous monitoring of physiological parameters: flower opening, fresh weight, water uptake, and gray mold incidence.
Validation of VMS measurements against traditional observational methods (OBS).

Main Results:

The VMS accurately and consistently measured key physiological factors in cut roses.
VMS measurements showed a strong correlation with observational data (OBS).
YOLOv8 achieved high object detection accuracy (90% mAP0.5), validating its effectiveness in VL evaluation.

Conclusions:

The VMS provides a highly effective tool for evaluating post-harvest quality and VL in cut roses.
The system's ability for early detection of physiological changes and disease is valuable for cut flower management.
The VMS technology can be applied to flower breeding programs for rapid assessment of traits like VL and disease resistance.