Detection method of slight bruises of apples based on hyperspectral imaging and RELIEF-extreme learning machine
In order to realize the rapid and nondestructive recognition of slight bruises of apples, a hyperspectral imaging technique (400-1 000 nm) was used. Hyperspectral images of sound and different damage time of Fuji apples were collected, and the average spectral reflectance and entropy were extracted...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Zhejiang University Press
2019-02-01
|
| Series: | 浙江大学学报. 农业与生命科学版 |
| Subjects: | |
| Online Access: | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2017.09.043 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In order to realize the rapid and nondestructive recognition of slight bruises of apples, a hyperspectral imaging technique (400-1 000 nm) was used. Hyperspectral images of sound and different damage time of Fuji apples were collected, and the average spectral reflectance and entropy were extracted from the region of interest (ROI) of the image. All the samples were divided into training set and test set (2∶1). The characteristic wavebands extracted based on the spectral average reflectance and entropy using RELIEF algorithm were 17, 30, 35, 51, 61, 66, 94 and 120, respectively. Then, based on full wavebands and characteristic wavebands, an extreme learning machine (ELM) model was built, as comparison with support vector machine (SVM) and K- mean algorithm. The results showed that the recognition accuracy of ELM model for the test set based on the full wavebands was 94.44%, and the accuracy of the Re-ELM model based on the characteristic wavebands was 96.67%, and the accuracy of the Re-SVM and Re-K mean models for the characteristic wavebands were 92.22% and 91.67%, respectively, which demonstrated that the Re-ELM was a more effective method for the bruise apple classification. Subsequently, an apple damage detection algorithm based on the characteristic wavebands and image processing was proposed, which performed an independent component algorithm (ICA) transformation of the characteristic wavebands, and selected the third component image of the ICA transformation, and used adaptive threshold segmentation to obtain the bruise area on apples. The final detection accuracy of apple damage detection algorithm based on the image processing technology was over 94%, which indicates that the algorithm is efficient for identifying slight bruises of apples. |
|---|---|
| ISSN: | 1008-9209 2097-5155 |