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Light Sources in Hyperspectral Imaging Simultaneously Influence Object Detection Performance and Vase Life of Cut

Yong-Tae Kim¹, Ji Yeong Ham¹, Byung-Chun In¹

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

Plants (Basel, Switzerland)

|January 28, 2026

Summary

This summary is machine-generated.

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Investigating light sources for hyperspectral imaging (HSI) of cut roses revealed halogen lamps yield high-quality spectral data and object detection but reduce flower vase life. An alternative two-phase illumination approach is proposed for optimized monitoring.

Area of Science:

Plant science
Spectroscopy
Artificial intelligence

Background:

Hyperspectral imaging (HSI) is a noncontact technique for plant condition assessment using light reflectance.
Cut roses are sensitive to abiotic stresses, impacting their vase life (VL).
Deep learning models require high-quality spectral data for accurate plant condition prediction.

Purpose of the Study:

To investigate the effects of four light sources (halogen, incandescent, fluorescent, LED) on hyperspectral image quality and cut rose vase life (VL).
To compare cultivar-specific reflectance characteristics of 'All For Love' and 'White Beauty' roses under different illuminations.
To determine the optimal illumination strategy for deep learning-based quality assessment in cut flowers.

Main Methods:

Keywords:

cut roses deep learning hyperspectral camera imaging object detection vase life

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Hyperspectral imaging (HSI) was performed on 40 cut roses across four time points under four different light sources.

Object detection performance (ODP) was evaluated using YOLOv11x models, with mean Average Precision (mAP@0.5) as the metric.

Petal temperature and vase life (VL) were measured to assess the impact of light sources on flower quality.

Main Results:

Halogen (HAL) lamps produced high-quality spectral images (mAP@0.5 83-85%) but increased petal temperature, shortening VL by 1-2.5 days.
Incandescent (INC) lamps resulted in lower image quality and ODP (mAP@0.5 69-74%) with minimal impact on VL.
Fluorescent (FLU) and LED lights showed minor effects on VL but generated spectral peaks, decreasing ODP (mAP@0.5 60-75%).

Conclusions:

Light source selection critically impacts both hyperspectral image quality for deep learning and the vase life of cut roses.
A two-phase illumination strategy, using HAL for initial spectral analysis and LED for routine monitoring, is proposed.
Optimizing illumination in HSI is crucial for developing automated quality sorting systems in the cut flower industry.