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Agriculture
Volume 13
Issue 9
10.3390/agriculture13091673
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Article

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

by
Cuiling Li
1,2,
Xiu Wang
1,3,
Liping Chen
3,*,
Xueguan Zhao
1,3,
Yang Li
1,
Mingzhou Chen
1,
Haowei Liu
1 and
Changyuan Zhai
1,2,*
1
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
2
Nongxin (Nanjing) Smart Agriculture Research Institute Co., Ltd., Nanjing 211800, China
3
National Engineering Research Center of Intelligent Equipment for Agriculture (NERCIEA), Beijing 100097, China
*
Authors to whom correspondence should be addressed.
Agriculture 2023, 13(9), 1673; https://doi.org/10.3390/agriculture13091673
Submission received: 19 July 2023 / Revised: 11 August 2023 / Accepted: 15 August 2023 / Published: 24 August 2023
(This article belongs to the Special Issue Diseases Diagnosis, Prevention and Weeds Control in Crops)
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Abstract

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.
Keywords: asparagus stem blight; hyperspectral imaging; disease grading; PCA; BPNN; ELM asparagus stem blight; hyperspectral imaging; disease grading; PCA; BPNN; ELM

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MDPI and ACS Style

Li, C.; Wang, X.; Chen, L.; Zhao, X.; Li, Y.; Chen, M.; Liu, H.; Zhai, C. Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns. Agriculture 2023, 13, 1673. https://doi.org/10.3390/agriculture13091673

AMA Style

Li C, Wang X, Chen L, Zhao X, Li Y, Chen M, Liu H, Zhai C. Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns. Agriculture. 2023; 13(9):1673. https://doi.org/10.3390/agriculture13091673

Chicago/Turabian Style

Li, Cuiling, Xiu Wang, Liping Chen, Xueguan Zhao, Yang Li, Mingzhou Chen, Haowei Liu, and Changyuan Zhai. 2023. "Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns" Agriculture 13, no. 9: 1673. https://doi.org/10.3390/agriculture13091673

APA Style

Li, C., Wang, X., Chen, L., Zhao, X., Li, Y., Chen, M., Liu, H., & Zhai, C. (2023). Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns. Agriculture, 13(9), 1673. https://doi.org/10.3390/agriculture13091673

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