超光谱成像中的光源同时影响切割的物体检测性能和花瓶寿命
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
概括
研究用于切割的高光谱成像 (HSI) 的光源,揭示了素灯能产生高质量的光谱数据和物体检测,但会减少花瓶寿命. 为优化监控,建议采用另一种两相照明方法.
科学领域:
- 植物科学 植物科学
- 频谱学是一种光谱学.
- 人工智能的人工智能是人工智能.
背景情况:
- 超光谱成像 (HSI) 是一种非接触技术,用于使用光反射率进行植物状况评估.
- 切割对非生物压力敏感,影响其花瓶寿命 (VL).
- 深度学习模型需要高质量的光谱数据来准确预测植物状况.
研究的目的:
- 研究四种光源 (素,白灯,光灯,LED) 对高光谱图像质量和切割花瓶寿命 (VL) 的影响.
- 为了比较不同种类的"All For Love"和"White Beauty"在不同的照明条件下的特定品种的反射特性.
- 确定最佳的照明策略,以深度学习为基础,对切花进行质量评估.
主要方法:
- 超光谱成像 (HSI) 在四个不同的光源下,在四个时间点进行了40个切割的.
- 使用YOLOv11x模型评估了对象检测性能 (ODP),以平均平均精度 (mAP@0.5) 为指标.
- 测量了花温度和花瓶寿命 (VL),以评估光源对花质量的影响.
主要成果:
- 素 (HAL) 灯产生了高质量的光谱图像 (mAP@0.5 83-85%),但增加了花温度,使VL缩短了1-2.5天.
- 光灯 (INC) 导致图像质量和ODP (mAP@0.5 69-74%) 降低,对VL的影响最小.
- 光灯 (FLU) 和LED灯对VL产生了轻微影响,但产生了光谱峰值,降低了ODP (mAP@0.5 60-75%).
结论:
- 光源的选择对深度学习的高光谱图像质量和切割的花瓶寿命都产生了重大影响.
- 建议采用双相照明策略,使用HAL进行初始光谱分析,使用LED进行常规监测.
- 在HSI中优化照明对于在切花行业开发自动化质量分类系统至关重要.
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