Cucumber powdery mildew detection method based on hyperspectra-terahertz

Xiaodong Zhang; Pei Wang; Yafei Wang; Lian Hu; Xiwen Luo; Hanping Mao; Baoguo Shen

doi:10.3389/fpls.2022.1035731

Cucumber powdery mildew detection method based on hyperspectra-terahertz

Front Plant Sci. 2022 Sep 29:13:1035731. doi: 10.3389/fpls.2022.1035731. eCollection 2022.

Authors

Xiaodong Zhang^{1

2}, Pei Wang^{1

2}, Yafei Wang^{1

2}, Lian Hu³, Xiwen Luo³, Hanping Mao^{1

2}, Baoguo Shen^{1

2}

Affiliations

¹ College of Agricultural Engineering, Jiangsu University, Zhenjiang, China.
² Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang, China.
³ Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou, China.

Abstract

To explore the use of information technology in detecting crop diseases, a method based on hyperspectra-terahertz for detecting cucumber powdery mildew is proposed. Specifically, a method of effective hyperspectrum establishment, a method of spectral preprocessing, a method of selecting the feature wavelength, and a method of establishing discriminant models are studied. Firstly, the effective spectral information under visible light and near infrared is preprocessed by Savitzky-Golay (SG) smoothing, discrete wavelet transform, and move sliding window, which determine the optimal preprocessing method to be wavelet transform. Then stepwise discriminant analysis is used to select the feature wavelengths in the visible and near-infrared bands, forming the feature space. According to the features, a linear discriminant model is established for the wave bands, and the average recognition rate of cucumber powdery mildew is 93% in the whole wave band. The preprocessing method of terahertz data, the screening method of terahertz effective spectrum, the selection method of feature wavelength and the establishment method of classification model are studied. Python 3.8 is used to preprocess the terahertz raw data and establish the terahertz effective spectral data set for subsequent processing. Through iterative variable subset optimization - iterative retaining informative variables (IVSO-IRIV), the terahertz effective spectrum is screened twice to form the terahertz feature space. After that, the optimal regularization parameter and regularization solution methods are selected, and a sparse representation classification model is established. The accuracy of cucumber powdery mildew identification under the terahertz scale is 87.78%. The extraction and analysis methods of terahertz and hyperspectral feature images are studied, and more details of lesion samples are restored. Hence, the use of hyperspectral and terahertz technology can realize the detection of cucumber powdery mildew, which provides a basis for research on the hyperspectral and terahertz technology in detection of crop diseases.

Keywords: cucumber; disease detection; hyperspectral; powdery mildew; terahertz.