Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder
Most leaf chlorophyll predictions based on digital image analyzes are modeled by manual extraction features and traditional machine learning methods. In this study, a series of image preprocessing operations, such as image threshold segmentation, noise processing, and background separation, were per...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2019-01-01
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| Series: | International Journal of Food Properties |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/10942912.2019.1675692 |