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Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNets

文献类型： 外文期刊

作者： Zhao, Xin 1 ; Zhang, Jianpei 1 ; Yang, Jing 1 ; Ma, Bo 3 ; Liu, Rui 4 ; Hu, Jifang 3 ;

作者机构： 1.Harbin Engn Univ, Coll Comp Sci & Technol, Harbin 150086, Peoples R China

2.Qiqihar Univ, Coll Comp & Control Engn, Qiqihar 161006, Peoples R China

3.Heilongjiang Acad Agr Sci, Qiqihar Branch, Qiqihar 161006, Peoples R China

4.Heilongjiang Bayi Agr Univ, Coll Agr Engn, Daqing 163000, Peoples R China

关键词： japonica rice; Raman spectroscopy; Python; capsule networks; growth duration

期刊名称：PLANTS-BASEL （ 影响因子：4.658； 五年影响因子：4.827 ）

ISSN：

年卷期： 2022 年 11 卷 12 期

页码：

收录情况： SCI

摘要： 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.

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