使用VSWIR光谱学在冬季小麦中进行宏观营养素和微量营养素分析
Anmol Kaur Gill1, Srishti Gaur1, Clay Sneller2
1Department of Food, Agricultural, and Biological Engineering, Ohio State University, Columbus, OH, United States.
Frontiers in plant science
|November 15, 2024
概括
可见到短波红外 (VSWIR) 反射率准确地预测冬季小麦叶子的营养和水分. 这种高通量方法可以为精准农业和育种计划进行快速,非破坏性的现场评估.
科学领域:
- 农业科学 农业科学
- 植物生理学 植物生理学
- 遥感 遥感 遥感 遥感
背景情况:
- 精确量化叶子营养素对于优化作物健康和冬季小麦产量至关重要.
- 传统的营养分析方法往往是破坏性的,耗时的,不适合大规模的现场评估.
- 可见到短波红外光谱 (VSWIR) 为评估植物生理状态提供了一种潜在的非破坏性方法.
研究的目的:
- 评估叶子级VSWIR反射在预测冬季小麦中宏观营养素,微量营养素和水分度方面的有效性.
- 开发和验证使用部分最小平方回归 (PLSR) 的预测模型,并探索不同的组件选择方法.
- 评估VSWIR光谱在小麦育种和精准农业中进行高通量,非破坏性营养分析的潜力.
主要方法:
- 在两个生长季节收集了360个冬季小麦叶样本,用于VSWIR反射度测量 (3502,500nm) 和化学分析.
- 开发了使用部分最小平方回归 (PLSR) 的预测模型,并采用了三个组件选择方法:绝对最小的PRESS,对PRESS的向后代和Van der Voet的随机t-test.
- 通过交叉验证评估模型性能,并通过预测 (VIP) 评分中的变量重要性检查光谱区域的重要性.
主要成果:
- 逆向代方法通常在模型性能和复杂性之间提供了良好的平衡.
- 对于一些营养和水分含量,包括 (0.84), (0.75), (0.78),水分 (0.84) 等,可以达到很高的预测准确度 (R2值).
- 变量重要性分析确定了对模型预测能力有贡献的关键光谱区域.
结论:
- 叶子级VSWIR反射率与PLSR相结合,是一种强大的高通量方法,用于量化冬季小麦中的多种叶子营养素和水分.
- 这种非破坏性的方法促进了快速而精确的现场评估,支持精准农业,提高了小麦育种计划的效率.
- 这项研究证明了VSWIR光谱技术在解决营养缺乏和优化作物管理策略方面的潜力.
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