Monitoring of Wheat Scab Using the Specific Spectral Index from ASD Hyperspectral Dataset

Linsheng Huang,Tingguang Hu,Hansu Zhang,Wenjuan Ding,Jinling Zhao,Wenjiang Huang

doi:10.1155/2019/9153195

Abstract

It is highly important to accurately monitor wheat scab and provide technical guidance for the crop pests and diseases. In this study, relevant analysis was performed among spectral reflectance, first-derivate data, and the disease severity data through ASD hyperspectral data. Two sensitive spectral wavelength ranges of 450–488 nm and 500–540 nm were selected. Then, a new wheat scab index (WSI) consisting of the two bands was proposed. The inversion models of the scab severities were comparatively built by unitary linear regression and multiple stepwise regression techniques. The results showed that the WSI had a significant linear relationship with severity of disease compared with other commonly used spectral indices. The fitting R2, testing R2, and RMSE were 0.73, 0.70, and 13.41, respectively. The multiple stepwise regression model established using the WSI, SDg/SDb, NBNDVI, and SDg as independent variables was better than the single-variable model. Our results suggest that WSI can be used to provide scientific guidance for monitoring and precise management of wheat scab disease.

Highlights

China is a big agricultural country with vast land and abundant agricultural resources
According to the statistics, when the severe occurrence of wheat scab disease is about 50%∼100%, the yield can be reduced by 40% [2]
Scab is a typical ear disease, so a proprietary index that can be used to monitor wheat ear-scale scab is desirable. erefore, based on previous studies, our study aimed (1) to identify wavebands that are sensitive to wheat scab at ear scale; (2) to construct a new spectral index (WSI) for characterizing the spectral changes caused by scab infestation; and (3) to evaluate the performance of the proposed wheat scab index (WSI) for retrieving scab severities using linear regression method

Summary

Introduction

China is a big agricultural country with vast land and abundant agricultural resources. Hyperspectral data with abundant band information and high resolution were used to detect the spectral response to diseases in more studies [7]. Many scholars used the hyperspectral data to analyze the spectral reflectance and found the sensitive bands for disease identification. Zheng et al [12] proved that the three-band spectral indices PRI (570, 525, and 705 nm) and ARI (860, 790, and 750 nm) are optimal for monitoring yellow rust infection at different growth stages. Erefore, based on previous studies, our study aimed (1) to identify wavebands that are sensitive to wheat scab at ear scale; (2) to construct a new spectral index (WSI) for characterizing the spectral changes caused by scab infestation; and (3) to evaluate the performance of the proposed WSI for retrieving scab severities using linear regression method Scab is a typical ear disease, so a proprietary index that can be used to monitor wheat ear-scale scab is desirable. erefore, based on previous studies, our study aimed (1) to identify wavebands that are sensitive to wheat scab at ear scale; (2) to construct a new spectral index (WSI) for characterizing the spectral changes caused by scab infestation; and (3) to evaluate the performance of the proposed WSI for retrieving scab severities using linear regression method

Materials and Methods

Data Analysis

Results and Discussion