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生物物理参数的非侵入性评估,分类和预测使用反射超光谱

Renan Falcioni¹, Glaucio Leboso Alemparte Abrantes Dos Santos¹, Luis Guilherme Teixeira Crusiol²

¹Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil.

Plants (Basel, Switzerland)

|July 14, 2023

概括

超光谱成像使用短波红外波段和机器学习准确预测烟草植物的生长. 这种非侵入性技术增强了精密农业的高通量表型化.

科学领域:

植物科学植物科学
遥感遥感遥感遥感
频谱学是一种光谱学.

背景情况:

对于精准农业而言,对植物形态参数的非侵入性监测至关重要.
超频谱技术为这种监控提供了一个有希望的途径.
了解植物对环境因素的反应,如光和吉伯酸 (GA3) 是至关重要的.

研究的目的:

评估UV-VIS-NIR-SWIR高光谱数据在*Nicotiana tabacum*L.L.L.中进行非侵入性预测形态参数的潜力.
确定最佳的光谱带和植被指数,用于监测植物生长.
评估人工智能 (AI) 和机器学习 (ML) 算法的有效性,以分类不同处理的植物.

主要方法:

收集了在不同光和GA3度下种植的烟草植物的UV-VIS-NIR-SWIR反射率超谱数据.
应用多变量分析,包括部分最小平方回归 (PLSR),以将光谱数据与形态参数 (高度,叶面积,能量产量,生物质) 相对应.
利用AI/ML算法 (神经网络,梯度增强) 来进行植物分类,并分析植物指数,如光化学反射率指数 (PRI).

主要成果:

短波红外 (SWIR) 波段 (SWIR1,SWIR2) 与植物生长参数的相关性最强.
在基于GA3度的植物分类中,AI/ML算法实现了超过99.1%的准确性.
光化学反射率指数 (PRI) 与所有评估的植物表型的相关性最强.
PLSR模型有效预测了具有高R2CV和RDPP值的形态属性.

结论:

SWIR超谱频段对于开发遥感方法来估计植物形态参数非常有价值.
超频谱技术与AI/ML相结合,使高通量表型化系统中的快速,准确,非侵入性监测成为可能.
这些发现支持数字和精准农业技术的进步.

关键词:

算法算法是一种算法.生物物理参数生物物理参数吉伯雷林斯 (Gibberellins) 是一个小岛.增长和发展的增长和发展.部分最小平方回归.植物表型化植物表型化植被指数植被指数在波长的波长.

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