超光谱成像系统的质量保证,用于神经网络支持的植物表型化
Justus Detring1, Abel Barreto2, Anne-Katrin Mahlein2
1Institute of Sugar Beet Research, Göttingen, Niedersachsen, 37079, Germany. Detring@ifz-goettingen.de.
Plant methods
|December 20, 2024
概括
超光谱成像 (HSI) 系统的新质量保证管道确保了精确的植物表型. 这种方法可以使用卷积神经网络 (CNN) 来对像甜菜这样的作物进行可靠的疾病严重程度估计.
科学领域:
- 农业科学 农业科学
- 光学工程是指光学工程.
- 植物病理学 植物病理学
背景情况:
- 超光谱成像 (HSI) 对于植物表型定型至关重要,但需要强大的质量保证.
- 现有的HSI系统需要标准化管道来评估空间,光谱和照明质量.
- 调查植物疾病,如Cercospora叶斑 (CLS) 需要准确的HSI数据.
研究的目的:
- 为手持式HSI系统开发和应用一个易于使用的质量保证管道.
- 提出一种分析质量保证HSI数据的方法,用于监测植物疾病进展.
- 评估HSI的适用性,以估计糖甜菜植物中CLS的严重程度.
主要方法:
- 使用基于正弦波的空间频率响应 (s-SFR) 评估空间精度.
- 通过将HSI系统测量与非成像光谱仪相关联来评估光谱准确性.
- 分析了集成的照明效应,并测试了用于CLS检测的卷积神经网络 (CNN).
主要成果:
- HSI和光谱仪测量之间的高相关性 (r>0.99) 证实了光谱准确性.
- 空间分辨率极限几乎达到了,检测到轻微的度变化.
- 集成的LED照明导致光谱扭曲;外部照明改善了基于CNN的CLS严重性估计结果.
结论:
- 开发的质量保证管道有效评估了手持式HSI系统.
- s-SFR分析和仪器间频谱相关性是有价值的质量保证指标.
- 即使是照明对于HSI来说也是至关重要的;尽管系统有局限性,但高光谱精度能够使用CNN进行准确的CLS进展分析.
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