Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns
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...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/13/9/1673 |
| _version_ | 1827727660976963584 |
|---|---|
| author | Cuiling Li Xiu Wang Liping Chen Xueguan Zhao Yang Li Mingzhou Chen Haowei Liu Changyuan Zhai |
| author_facet | Cuiling Li Xiu Wang Liping Chen Xueguan Zhao Yang Li Mingzhou Chen Haowei Liu Changyuan Zhai |
| author_sort | Cuiling Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T23:09:31Z |
| format | Article |
| id | doaj.art-6dc759f8009843e99d86db4024ab0568 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-10T23:09:31Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-6dc759f8009843e99d86db4024ab05682023-11-19T09:05:45ZengMDPI AGAgriculture2077-04722023-08-01139167310.3390/agriculture13091673Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus CrownsCuiling Li0Xiu Wang1Liping Chen2Xueguan Zhao3Yang Li4Mingzhou Chen5Haowei Liu6Changyuan Zhai7Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaNational Engineering Research Center of Intelligent Equipment for Agriculture (NERCIEA), Beijing 100097, ChinaIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaThis 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.https://www.mdpi.com/2077-0472/13/9/1673asparagus stem blighthyperspectral imagingdisease gradingPCABPNNELM |
| spellingShingle | Cuiling Li Xiu Wang Liping Chen Xueguan Zhao Yang Li Mingzhou Chen Haowei Liu Changyuan Zhai Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns Agriculture asparagus stem blight hyperspectral imaging disease grading PCA BPNN ELM |
| title | Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns |
| title_full | Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns |
| title_fullStr | Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns |
| title_full_unstemmed | Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns |
| title_short | Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns |
| title_sort | grading and detection method of asparagus stem blight based on hyperspectral imaging of asparagus crowns |
| topic | asparagus stem blight hyperspectral imaging disease grading PCA BPNN ELM |
| url | https://www.mdpi.com/2077-0472/13/9/1673 |
| work_keys_str_mv | AT cuilingli gradinganddetectionmethodofasparagusstemblightbasedonhyperspectralimagingofasparaguscrowns AT xiuwang gradinganddetectionmethodofasparagusstemblightbasedonhyperspectralimagingofasparaguscrowns AT lipingchen gradinganddetectionmethodofasparagusstemblightbasedonhyperspectralimagingofasparaguscrowns AT xueguanzhao gradinganddetectionmethodofasparagusstemblightbasedonhyperspectralimagingofasparaguscrowns AT yangli gradinganddetectionmethodofasparagusstemblightbasedonhyperspectralimagingofasparaguscrowns AT mingzhouchen gradinganddetectionmethodofasparagusstemblightbasedonhyperspectralimagingofasparaguscrowns AT haoweiliu gradinganddetectionmethodofasparagusstemblightbasedonhyperspectralimagingofasparaguscrowns AT changyuanzhai gradinganddetectionmethodofasparagusstemblightbasedonhyperspectralimagingofasparaguscrowns |