使用光谱和色彩空间数据与AutoML相结合,预测单核玉米的总安托西亚宁含量
Umut Songur1, Sertuğ Fidan2, Ezgi Alaca Yıldırım3
1Department of Field Crops, Faculty of Agriculture, Çanakkale Onsekiz Mart University, 17100 Çanakkale, Türkiye.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
自动机器学习 (AutoML) 准确地预测使用光谱和图像数据在单个玉米核中的安托西亚宁含量. 这种非破坏性方法通过有效选可取特征来增强植物育种.
科学领域:
- 农业科学 农业科学
- 植物育种 植物育种
- 频谱学是一种光谱学.
背景情况:
- 在单个玉米核中非破坏性,无化学物质的安托西亚宁含量的确定对于植物育种至关重要.
- 以前的方法依赖于近红外反射 (NIR) 光谱和色度测量,通常使用传统的建模技术.
研究的目的:
- 采用自动机器学习 (AutoML) 框架来预测各个玉米核中的安托西亚宁含量.
- 与单核分析的传统方法相比,评估AutoML的有效性.
主要方法:
- 从40个基因型的200个玉米核收集了光谱 (NIR) 和数字图像 (RGB,HSV,LAB) 数据.
- 构建了十个数据集,结合了不同的数据特征和内核定向 (胚胎上/下).
- 利用AutoML评估九个算法和部分最小平方回归 (PLSR) 作为基准,开发了1918年的预测模型.
主要成果:
- 内核定向对模型性能和异常值检测产生了重大影响.
- 通过使用RGB数据集用于胚胎升级内核和RGB+HSV+LAB+NIR数据集用于胚胎下降内核来实现最佳预测.
- 自动ML模型的性能一般优于传统的PLSR模型.
结论:
- 自动ML提供了一种高效和自动化的方法来预测单核安托西亚尼含量.
- 这种非破坏性选工具可以通过快速评估核子特征来加速植物育种计划.
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