AUTHOR=Wang Wei , Sun Na , Bai Bin , Wu Hao , Cheng Yukun , Geng Hongwei , Song JiKun , Zhou JinPing , Pang Zhiyuan , Qian SongTing , Zeng Wanyin 

TITLE=Prediction of wheat SPAD using integrated multispectral and support vector machines

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1405068

DOI=10.3389/fpls.2024.1405068

ISSN=1664-462X

ABSTRACT=Rapidly obtaining the chlorophyll content of crop leaves is of great significance for timely diagnosis of crop health status and guiding field management. Multispectral imagery obtained from Unmanned aerial vehicle (UAV) is being used to remotely sense the SPAD (Soil and Plant Analyzer Development) values of wheat crops. However, existing research has not yet taken into account the influence of different growth stages and crop populations on the accuracy of SPAD estimation. In this study, 300 materials material of winter wheat natural populations in Xinjiang from 2020 to 2022 were selected as the research objects. 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 of the model were screened, and support vector machine (SVM) model was constructed to estimate the SPAD values during the heading, flowering, and filling stages under different water stress. The aim was to provide 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 r was 0.69-0.79, RMSE was 2.30-12.94, RE was 0.10%-1.30%.This study demonstrated that the optimal combination of feature selection methods and machine learning algorithms can lead to 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 values.