Application of Support Vector Machine Regression and Partial Least-Square Regression Models for Predicting Sugarcane Leaf Nutrients Content Using Near Infra-Red Spectroscopy-Reference-Cited by-同舟云学术

Application of Support Vector Machine Regression and Partial Least-Square Regression Models for Predicting Sugarcane Leaf Nutrients Content Using Near Infra-Red Spectroscopy

Published:2023-10-10 Issue: Volume: Page:1-12
ISSN:0010-3624
Container-title:Communications in Soil Science and Plant Analysis
language:en
Short-container-title:Communications in Soil Science and Plant Analysis

Author:

Mitku Aweke A.¹²,Titshall Louis³⁴,Zewotir Temesgen¹,North Delia¹

Affiliation:

1. School of Mathematics, Statistics and Computer Science, College of Agriculture Engineering and Science, University of KwaZulu-Natal, Durban, South Africa

2. Department of Statistics, Bahir Dar University, Bahir Dar, Ethiopia

3. Crop Nutrition and Soils, Plant and Environment Resource Centre, South Africa Sugarcane Research Institute, Mount Edgecombe, South Africa

4. School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Scottsville, South Africa

Funder

the South African Sugar Association

Publisher

Informa UK Limited

Subject

Soil Science,Agronomy and Crop Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/00103624.2023.2265426

Reference28 articles.

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4. Partial least squares: a versatile tool for the analysis of high-dimensional genomic data

5. Near‐Infrared Reflectance Spectroscopy Analysis of Phosphorus in Sugarcane Leaves

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