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一种多目标回归方法,用高光谱成像技术预测番茄叶中的元素度

Andrés Aguilar Ariza¹, Naoyuki Sotta¹, Toru Fujiwara¹

¹Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.

Plant phenomics (Washington, D.C.)

|April 17, 2024

概括

一种新的多目标回归方法显著提高了使用超光谱成像的植物元素度预测准确度. 这种方法提高了作物的营养监测,通过实现10个基本元素的更高准确度.

科学领域:

农业科学农业科学
遥感遥感遥感遥感
机器学习机器学习

背景情况:

超光谱成像与机器学习模型相结合,是一种快速,廉价的植物营养状况监测方法.
当前的单目标回归模型预测了单个元素的度,但对不同元素的准确性是可变的.
同时提高多个元素的预测准确度对于全面的作物营养评估至关重要.

研究的目的:

为了提高植物元素度预测的准确性.
评估一种新的多目标回归方法,该方法可以顺序增强高光谱成像特征,并预测元素度.
为了比较多目标方法的性能与传统的单目标回归来预测番茄叶中的17个元素.

主要方法:

开发了一个多目标回归模型,通过顺序增强高光谱成像数据与预测的元素度.
使用增强功能训练了五种不同的机器学习模型.
预测了番茄叶中的17种元素的度,并将结果与单个目标回归进行了比较.

主要成果:

多目标回归方法显著提高了10个元素的预测准确度,包括Mg,P,S,Mn,Fe,Co,Cu,Sr,Mo和Cd.
对几个元素的确定系数 (R2) 显著增加:Mn (12.5%),Cu (10.3%),Co (11%),Fe (10%) 和Mg (8.4%).
在预测元素度方面表现优于单个目标回归,突出显示了顺序增强方法的有效性.

结论:

拟议的多目标回归方法在从高光谱数据中预测植物元素度方面取得了重大进展.
与单一目标方法相比,这种方法提供了更准确和更全面的作物营养状况评估.
这些发现支持采用多目标回归来改善农业监测和管理.

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