Moisture Content Quantization of Masson Pine Seedling Leaf Based on Stacked Autoencoder with Near-Infrared Spectroscopy
Masson pine is widely planted in southern China, and moisture content of the pine seedling leaves is an important index for evaluating the vigor of seedlings. For precisely predicting leaf moisture content, near-infrared spectroscopy analysis is applied in the experiment, which is a cost-effective,...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2018/8696202 |
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| author | Chao Ni Yun Zhang Dongyi Wang |
| author_facet | Chao Ni Yun Zhang Dongyi Wang |
| author_sort | Chao Ni |
| collection | DOAJ |
| description | Masson pine is widely planted in southern China, and moisture content of the pine seedling leaves is an important index for evaluating the vigor of seedlings. For precisely predicting leaf moisture content, near-infrared spectroscopy analysis is applied in the experiment, which is a cost-effective, high-speed, and noninvasive material content prediction tool. To further improve the spectroscopy analysis accuracy, in this study, a new analysis model is proposed which integrates a stacked autoencoder for extracting hierarchical output-related features layer by layer and a support vector regression model to leverage these features for precisely predicting moisture contents. Compared with traditional spectroscopy analysis method like partial least squares regression and basic support vector regression, the proposed model shows great superiority for leaf moisture content prediction, with R2 value 0.9946 and root-mean squared error (RMSE) value 0.1636 in calibration set and R2 value 0.9621 and RMSE 0.4249 in prediction set. |
| format | Article |
| id | doaj-art-de726a51c9d3471b80983cf9f54b81f7 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-de726a51c9d3471b80983cf9f54b81f72025-02-03T05:45:50ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552018-01-01201810.1155/2018/86962028696202Moisture Content Quantization of Masson Pine Seedling Leaf Based on Stacked Autoencoder with Near-Infrared SpectroscopyChao Ni0Yun Zhang1Dongyi Wang2School of Mechanical and Electrical Engineering, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Mechanical and Electrical Engineering, Nanjing Forestry University, Nanjing 210037, ChinaBio-Imaging and Machine Vision Lab, Fischell Department of Bioengineering, University of Maryland, College Park 20740, USAMasson pine is widely planted in southern China, and moisture content of the pine seedling leaves is an important index for evaluating the vigor of seedlings. For precisely predicting leaf moisture content, near-infrared spectroscopy analysis is applied in the experiment, which is a cost-effective, high-speed, and noninvasive material content prediction tool. To further improve the spectroscopy analysis accuracy, in this study, a new analysis model is proposed which integrates a stacked autoencoder for extracting hierarchical output-related features layer by layer and a support vector regression model to leverage these features for precisely predicting moisture contents. Compared with traditional spectroscopy analysis method like partial least squares regression and basic support vector regression, the proposed model shows great superiority for leaf moisture content prediction, with R2 value 0.9946 and root-mean squared error (RMSE) value 0.1636 in calibration set and R2 value 0.9621 and RMSE 0.4249 in prediction set.http://dx.doi.org/10.1155/2018/8696202 |
| spellingShingle | Chao Ni Yun Zhang Dongyi Wang Moisture Content Quantization of Masson Pine Seedling Leaf Based on Stacked Autoencoder with Near-Infrared Spectroscopy Journal of Electrical and Computer Engineering |
| title | Moisture Content Quantization of Masson Pine Seedling Leaf Based on Stacked Autoencoder with Near-Infrared Spectroscopy |
| title_full | Moisture Content Quantization of Masson Pine Seedling Leaf Based on Stacked Autoencoder with Near-Infrared Spectroscopy |
| title_fullStr | Moisture Content Quantization of Masson Pine Seedling Leaf Based on Stacked Autoencoder with Near-Infrared Spectroscopy |
| title_full_unstemmed | Moisture Content Quantization of Masson Pine Seedling Leaf Based on Stacked Autoencoder with Near-Infrared Spectroscopy |
| title_short | Moisture Content Quantization of Masson Pine Seedling Leaf Based on Stacked Autoencoder with Near-Infrared Spectroscopy |
| title_sort | moisture content quantization of masson pine seedling leaf based on stacked autoencoder with near infrared spectroscopy |
| url | http://dx.doi.org/10.1155/2018/8696202 |
| work_keys_str_mv | AT chaoni moisturecontentquantizationofmassonpineseedlingleafbasedonstackedautoencoderwithnearinfraredspectroscopy AT yunzhang moisturecontentquantizationofmassonpineseedlingleafbasedonstackedautoencoderwithnearinfraredspectroscopy AT dongyiwang moisturecontentquantizationofmassonpineseedlingleafbasedonstackedautoencoderwithnearinfraredspectroscopy |