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 |
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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 |
| _version_ | 1819049158698336256 |
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| author | Yingshu Peng Yi Wang |
| author_facet | Yingshu Peng Yi Wang |
| author_sort | Yingshu Peng |
| collection | DOAJ |
| description | 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 performed based on digital image processing technology to remove the background and noise interference. The intrinsic features of the leaf RGB image were automatically learned through a stacked sparse autoencoder (SSAE) network to obtain concise data features. Finally, a prediction model between the RGB image features of a leaf and its SPAD value (arbitrary units) was established to predict the chlorophyll content in the plant leaf. The results show that the accuracy and automation of the detection of chlorophyll content of the deep neural network in this study are higher than those of traditional machine learning methods. |
| first_indexed | 2024-12-21T11:27:42Z |
| format | Article |
| id | doaj.art-cea84b53ee27490caed30bffddd708c9 |
| institution | Directory Open Access Journal |
| issn | 1094-2912 1532-2386 |
| language | English |
| last_indexed | 2024-12-21T11:27:42Z |
| publishDate | 2019-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Food Properties |
| spelling | doaj.art-cea84b53ee27490caed30bffddd708c92022-12-21T19:05:37ZengTaylor & Francis GroupInternational Journal of Food Properties1094-29121532-23862019-01-012211720173210.1080/10942912.2019.16756921675692Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoderYingshu Peng0Yi Wang1Nanjing Forestry UniversityJiangsu Wiscom Technology Co. LtdMost 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 performed based on digital image processing technology to remove the background and noise interference. The intrinsic features of the leaf RGB image were automatically learned through a stacked sparse autoencoder (SSAE) network to obtain concise data features. Finally, a prediction model between the RGB image features of a leaf and its SPAD value (arbitrary units) was established to predict the chlorophyll content in the plant leaf. The results show that the accuracy and automation of the detection of chlorophyll content of the deep neural network in this study are higher than those of traditional machine learning methods.http://dx.doi.org/10.1080/10942912.2019.1675692pomegranate leaveschlorophyll contentimage processingstacked sparse autoencoderdeep learning |
| spellingShingle | Yingshu Peng Yi Wang Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder International Journal of Food Properties pomegranate leaves chlorophyll content image processing stacked sparse autoencoder deep learning |
| title | Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder |
| title_full | Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder |
| title_fullStr | Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder |
| title_full_unstemmed | Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder |
| title_short | Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder |
| title_sort | prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder |
| topic | pomegranate leaves chlorophyll content image processing stacked sparse autoencoder deep learning |
| url | http://dx.doi.org/10.1080/10942912.2019.1675692 |
| work_keys_str_mv | AT yingshupeng predictionofthechlorophyllcontentinpomegranateleavesbasedondigitalimageprocessingtechnologyandstackedsparseautoencoder AT yiwang predictionofthechlorophyllcontentinpomegranateleavesbasedondigitalimageprocessingtechnologyandstackedsparseautoencoder |