Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNets
Rice cultivation in cold regions of China is mainly distributed in Heilongjiang Province, where the growing season of rice is susceptible to low temperature and cold damage. Choosing and planting rice varieties with suitable GD according to the accumulated temperate zone is an important measure to p...
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| Format: | Article |
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MDPI AG
2022-06-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/11/12/1573 |
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| author | Xin Zhao Jianpei Zhang Jing Yang Bo Ma Rui Liu Jifang Hu |
| author_facet | Xin Zhao Jianpei Zhang Jing Yang Bo Ma Rui Liu Jifang Hu |
| author_sort | Xin Zhao |
| collection | DOAJ |
| description | Rice cultivation in cold regions of China is mainly distributed in Heilongjiang Province, where the growing season of rice is susceptible to low temperature and cold damage. Choosing and planting rice varieties with suitable GD according to the accumulated temperate zone is an important measure to prevent low temperature and cold damage. However, the traditional identification method of rice GD requires lots of field investigations, which are time consuming and susceptible to environmental interference. Therefore, an efficient, accurate, and intelligent identification method is urgently needed. In response to this problem, we took seven rice varieties suitable for three accumulated temperature zones in Heilongjiang Province as the research objects, and we carried out research on the identification of japonica rice GD based on Raman spectroscopy and capsule neural networks (CapsNets). The data preprocessing stage used a variety of methods (signal.filtfilt, difference, segmentation, and superposition) to process Raman spectral data to complete the fusion of local features and global features and data dimension transformation. A CapsNets containing three neuron layers (one convolutional layer and two capsule layers) and a dynamic routing protocol was constructed and implemented in Python. After training 160 epochs on the CapsNets, the model achieved 89% and 93% accuracy on the training and test datasets, respectively. The results showed that Raman spectroscopy combined with CapsNets can provide an efficient and accurate intelligent identification method for the classification and identification of rice GD in Heilongjiang Province. |
| first_indexed | 2024-03-09T22:42:45Z |
| format | Article |
| id | doaj.art-a6803ab1069641878356da73f3af7239 |
| institution | Directory Open Access Journal |
| issn | 2223-7747 |
| language | English |
| last_indexed | 2024-03-09T22:42:45Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
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| series | Plants |
| spelling | doaj.art-a6803ab1069641878356da73f3af72392023-11-23T18:34:46ZengMDPI AGPlants2223-77472022-06-011112157310.3390/plants11121573Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNetsXin Zhao0Jianpei Zhang1Jing Yang2Bo Ma3Rui Liu4Jifang Hu5College of Computer Science and Technology, Harbin Engineering University, Harbin 150086, ChinaCollege of Computer Science and Technology, Harbin Engineering University, Harbin 150086, ChinaCollege of Computer Science and Technology, Harbin Engineering University, Harbin 150086, ChinaQiqihar Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihar 161006, ChinaCollege of Agricultural Engineering, Heilongjiang Bayi Agricultural University, Daqing 163000, ChinaQiqihar Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihar 161006, ChinaRice cultivation in cold regions of China is mainly distributed in Heilongjiang Province, where the growing season of rice is susceptible to low temperature and cold damage. Choosing and planting rice varieties with suitable GD according to the accumulated temperate zone is an important measure to prevent low temperature and cold damage. However, the traditional identification method of rice GD requires lots of field investigations, which are time consuming and susceptible to environmental interference. Therefore, an efficient, accurate, and intelligent identification method is urgently needed. In response to this problem, we took seven rice varieties suitable for three accumulated temperature zones in Heilongjiang Province as the research objects, and we carried out research on the identification of japonica rice GD based on Raman spectroscopy and capsule neural networks (CapsNets). The data preprocessing stage used a variety of methods (signal.filtfilt, difference, segmentation, and superposition) to process Raman spectral data to complete the fusion of local features and global features and data dimension transformation. A CapsNets containing three neuron layers (one convolutional layer and two capsule layers) and a dynamic routing protocol was constructed and implemented in Python. After training 160 epochs on the CapsNets, the model achieved 89% and 93% accuracy on the training and test datasets, respectively. The results showed that Raman spectroscopy combined with CapsNets can provide an efficient and accurate intelligent identification method for the classification and identification of rice GD in Heilongjiang Province.https://www.mdpi.com/2223-7747/11/12/1573japonica riceRaman spectroscopyPythoncapsule networksgrowth duration |
| spellingShingle | Xin Zhao Jianpei Zhang Jing Yang Bo Ma Rui Liu Jifang Hu Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNets Plants japonica rice Raman spectroscopy Python capsule networks growth duration |
| title | Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNets |
| title_full | Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNets |
| title_fullStr | Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNets |
| title_full_unstemmed | Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNets |
| title_short | Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNets |
| title_sort | intelligent classification of japonica rice growth duration gd based on capsnets |
| topic | japonica rice Raman spectroscopy Python capsule networks growth duration |
| url | https://www.mdpi.com/2223-7747/11/12/1573 |
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