基于可见和近红外光谱的多变量堆叠回归管道估算马铃薯植物中相关宏微量元素

IF 12.4 Q1 AGRICULTURE, MULTIDISCIPLINARY
Reem Abukmeil , Ahmad Al-Mallahi , Felipe Campelo
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引用次数: 0

摘要

利用光谱学检测马铃薯植物营养状况的能力有几个优点,包括能够主动响应某些元素的缺乏。虽然迄今为止的研究主要集中在寻找基于其叶片反射率的元素的光谱特征,但尚未研究元素的光谱特征在估计其在植物中的浓度时彼此之间的影响。这项工作提出了一个堆叠回归模型的管道,能够准确地估计基于叶片反射率的营养物质浓度。利用两个生长季节收集的179份叶柄样本建立了一个数据集,包括11种营养物的化学浓度,光谱反射率在400 ~ 2500 nm之间。该管道由一个由多个单变量线性Lasso回归模型组成的底层组成,用于找到每种营养素的初始独立特征,然后是一层非线性模型,用于关联这些特征并在最终估计之前解释它们的相互依赖性。结果表明,在干燥和新鲜模式下,添加第二层可分别提高12种营养物质中10种和9种的预测性能,对锌、铁和铝等关键微量营养物质的预测性能有较大提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multivariate stacked regression pipeline to estimate correlated macro and micronutrients in potato plants using visible and near-infrared reflectance spectra
The ability to sense nutrient status in potato plants using spectroscopy has several merits including the ability to proactively respond to deficiencies of certain elements. While research so far has focused on finding spectral signatures of elements based on their foliar reflectance, the influence of the spectral signatures of the elements on each other in estimating their concentrations in the plant has not been investigated. This work presents a pipeline of stacked regression models capable of accurately estimating nutrient concentrations based on the foliar reflectance. A data set was built from 179 samples of petioles collected across two growing seasons, consisting of the chemical concentrations of 11 nutrients with spectral reflectance values between 400 and 2500 nm. The pipeline consisted of a base layer composed of a multiple univariate linear Lasso regression models to find the initial independent signatures of each nutrient, followed by a layer of nonlinear models to correlate these signatures and account for their interdependencies before finalizing the estimation. The results show that adding this second layer improved estimation performance for 10 and 9 nutrients out of 12 in the dried and fresh mode, respectively, with large improvements in predictive performance for some critical micronutrients such as Zn, Fe, and Al.
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来源期刊
Artificial Intelligence in Agriculture
Artificial Intelligence in Agriculture Engineering-Engineering (miscellaneous)
CiteScore
21.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
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