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Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns

文献类型： 外文期刊

作者： Li, Cuiling 1 ; Wang, Xiu 1 ; Chen, Liping 3 ; Zhao, Xueguan 1 ; Li, Yang 1 ; Chen, Mingzhou 1 ; Liu, Haowei 1 ; Zhai, Changyuan 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

2.Nongxin Nanjing Smart Agr Res Inst Co Ltd, Nanjing 211800, Peoples R China

3.Natl Engn Res Ctr Intelligent Equipment Agr NERCIE, Beijing 100097, Peoples R China

关键词： asparagus stem blight; hyperspectral imaging; disease grading; PCA; BPNN; ELM

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.6 ）

ISSN：

年卷期： 2023 年 13 卷 9 期

页码：

收录情况： SCI

摘要： This study adopted hyperspectral imaging technology combined with machine learning to detect the disease severity of stem blight through the canopy of asparagus mother stem. Several regions of interest were selected from each hyperspectral image, and the reflection spectra of the regions of interest were extracted. There were 503 sets of hyperspectral data in the training set and 167 sets of hyperspectral data in the test set. The data were preprocessed using various methods and the dimension was reduced using PCA. K-nearest neighbours (KNN), decision tree (DT), BP neural network (BPNN), and extreme learning machine (ELM) were used to establish a classification model of asparagus stem blight. The optimal model depended on the preprocessing methods used. When modeling was based on the ELM method, the disease grade discrimination effect of the FD-MSC-ELM model was the best with an accuracy (ACC) of 1.000, a precision (PREC) of 1.000, a recall (REC) of 1.000, an F1-score (F1S) of 1.000, and a norm of the absolute error (NAE) of 0.000, respectively; when the modeling was based on the BPNN method, the discrimination effect of the FD-SNV-BPNN model was the best with an ACC of 0.976, a PREC of 0.975, a REC of 0.978, a F1S of 0.976, and a mean square error (MSE) of 0.072, respectively. The results showed that hyperspectral imaging of the asparagus mother stem canopy combined with machine learning methods could be used to grade and detect stem blight in asparagus mother stems.

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