用于叶片养分吸收定量的光谱数据增强技术

IF 4.4 1区 农林科学 Q1 AGRICULTURAL ENGINEERING
R.C. Martins , C. Queirós , F.M. Silva , F. Santos , T.G. Barroso , R. Tosin , M. Cunha , M. Leão , M. Damásio , P. Martins , J. Silvestre
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引用次数: 0

摘要

数据匮乏是基于生理学的精准农业面临的一个障碍。利用可见光-近红外光谱测量养分吸收意味着要从低通量(如电感耦合等离子体光发射光谱)中收集光谱和成分数据。本文通过杂交技术介绍了光谱学中的数据扩增技术,可将真实世界的数据扩充为在统计上代表真实数据的合成数据集,从而实现宏量营养元素(氮、磷、钾、钙、镁和硒)和微量营养元素(铁、锰、锌、铜和硼)的量化。利用偏最小二乘法(PLS)、局部偏最小二乘法(LocPLS)和自学人工智能(SLAI)来确定扩展知识库的能力。仅使用真实世界数据(RWD)的 PLS 无法量化某些养分(葡萄叶片中的氮和铜,苹果树叶片中的钾、钙、镁、硒和铜)。该研究的合成数据集可以预测真实世界叶片中的大量营养素(氮、磷、钾、钙、镁和硫)的组成(皮尔逊相关系数 (R) ∼ 0.61-0.使用 LocPLS 和 SLAI 分析了葡萄叶片中的常量营养元素(氮、磷、钾、钙、镁和硫)和微量营养元素(铁、锰、锌、铜和硼)(皮尔逊系数相关性 (R) ∼ 0.61-0.94 和标准误差 (SE) ∼ 0.04-0.05%)(R ∼ 0.66-0.91 和 SE ∼ 0.88-3.98 ppm)。如果真实世界的对应数据代表性较低,合成数据集就会失去意义,导致苹果树叶片中宏量营养元素(R ∼ 0.51-0.72 和 SE ∼ 0.02-0.13% )和微量营养元素(R ∼ 0.53-0.76 和 SE ∼ 8.89-37.89 ppm)的定量不佳,并且无法实现 S 的定量(R = 0.37,SE = 0.01)。具有代表性的实际取样使得光谱学数据扩增在扩大知识库和营养定量方面非常有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Spectral data augmentation for leaf nutrient uptake quantification

Spectral data augmentation for leaf nutrient uptake quantification

Data scarcity is a hurdle for physiology-based precision agriculture. Measuring nutrient uptake by visible-near infrared spectroscopy implies collecting spectral and compositional data from low-throughput, such as inductively coupled plasma optical emission spectroscopy. This paper introduces data augmentation in spectroscopy by hybridisation for expanding real-world data into synthetic datasets statistically representative of the real data, allowing the quantification of macronutrients (N, P, K, Ca, Mg, and S) and micronutrients (Fe, Mn, Zn, Cu, and B). Partial least squares (PLS), local partial least squares (LocPLS), and self-learning artificial intelligence (SLAI) were used to determine the capacity to expand the knowledge base. PLS using only real-world data (RWD) cannot quantify some nutrients (N and Cu in grapevine leaves and K, Ca, Mg, S, and Cu in apple tree leaves). The synthetic dataset of the study allowed predicting real-world leaf composition of macronutrients (N, P, K, Ca, Mg and S) (Pearson coefficient correlation (R) ∼ 0.61–0.94 and standard error (SE) ∼ 0.04–0.05%) and micro-nutrients (Fe, Mn, Zn, Cu and B) (R ∼ 0.66–0.91 and SE ∼ 0.88–3.98 ppm) in grapevine leaves using LocPLS and SLAI. The synthetic dataset loses significance if the real-world counterpart has low representativity, resulting in poor quantifications of macronutrients (R ∼ 0.51–0.72 and SE ∼ 0.02–0.13%) and micronutrients (R ∼ 0.53–0.76 and SE ∼ 8.89–37.89 ppm), and not allowing S quantification (R = 0.37, SE = 0.01) in apple tree leaves. Representative real-world sampling makes data augmentation in spectroscopy very efficient in expanding the knowledge base and nutrient quantifications.

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来源期刊
Biosystems Engineering
Biosystems Engineering 农林科学-农业工程
CiteScore
10.60
自引率
7.80%
发文量
239
审稿时长
53 days
期刊介绍: Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.
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