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Prediction of wheat SPAD using integrated multispectral and support vector machines

Authors :
Wei Wang
Na Sun
Bin Bai
Hao Wu
Yukun Cheng
Hongwei Geng
JiKun Song
JinPing Zhou
Zhiyuan Pang
SongTing Qian
Wanyin Zeng
Source :
Frontiers in Plant Science, Vol 15 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

Abstract

Rapidly obtaining the chlorophyll content of crop leaves is of great significance for timely diagnosis of crop health and effective field management. Multispectral imagery obtained from unmanned aerial vehicles (UAV) is being used to remotely sense the SPAD (Soil and Plant Analyzer Development) values of wheat crops. However, existing research has not yet fully considered the impact of different growth stages and crop populations on the accuracy of SPAD estimation. In this study, 300 materials from winter wheat natural populations in Xinjiang, collected between 2020 to 2022, were analyzed. UAV multispectral images were obtained in the experimental area, and vegetation indices were extracted to analyze the correlation between the selected vegetation indices and SPAD values. The input variables for the model were screened, and a support vector machine (SVM) model was constructed to estimate SPAD values during the heading, flowering, and filling stages under different water stresses. The aim was to provide a method for the rapid acquisition of winter wheat SPAD values. The results showed that the SPAD values under normal irrigation were higher than those under water restriction. Multiple vegetation indices were significantly correlated with SPAD values. In the prediction model construction of SPAD, the different models had high estimation accuracy under both normal irrigation and water limitation treatments, with correlation coefficients of predicted and measured values under normal irrigation in different environments the value of r from 0.59 to 0.81 and RMSE from 2.15 to 11.64, compared to RE from 0.10% to 1.00%; and under drought stress in different environments, correlation coefficients of predicted and measured values of r was 0.69–0.79, RMSE was 2.30–12.94, and RE was 0.10%–1.30%. This study demonstrated that the optimal combination of feature selection methods and machine learning algorithms can lead to a more accurate estimation of winter wheat SPAD values. In summary, the SVM model based on UAV multispectral images can rapidly and accurately estimate winter wheat SPAD value.

Details

Language :
English
ISSN :
1664462X
Volume :
15
Database :
Directory of Open Access Journals
Journal :
Frontiers in Plant Science
Publication Type :
Academic Journal
Accession number :
edsdoj.7f4e7dd16cf14f60962150b345dc8ef1
Document Type :
article
Full Text :
https://doi.org/10.3389/fpls.2024.1405068