# Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNets
## 1 Introduction  
Heilongjiang Province is located in the black-soil zone of the Northeast Plain, one of the three major black-soil zones in the world. It is also the province with the largest rice planting area in China and occupies an important position in the world's rice market [1, 2]. The growth, development, and yield formation of rice are sensitive to temperature and light. Cold damage is one of the most important agrometeorological disasters for rice in cold regions. Rice cultivation in cold regions of China is mainly distributed in Heilongjiang Province [3, 4], which belongs to both a high-cold rice region and mid-high latitude region, and the growing season of rice is susceptible to low temperature and cold damage [5, 6, 7, 8]. According to the National Bureau of Statistics of China [9], from 2017 to 2019, the rice yield (kg/ha) in Heilongjiang Province was 7139.57, 7098.79, and 6986, respectively. Due to the impact of low temperature and cold damage, in recent years, rice yields in Heilongjiang Province have been declining at a rate of 1.59% [9]. The accumulated temperate zone is an important reference for the planting layout and variety selection of various crops in Heilongjiang Province [4]. Rice cultivation is mainly distributed in the 1 st-3 rd accumulatedtemperate zones of Heilongjiang Province. Different accumulated temperate zones have different requirements for GD of rice varieties. 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 [10; 11]. Japonica rice is a subspecies of rice that is resistant to low temperature and weak light, suitable for planting in Heilongjiang Province. It also has a low amylose content and good palatability [12]; therefore, rice breeding experts regard the accurate identification of GD of japonica rice varieties in cold regions as an important task. Some researchers have obtained a variety of rice phenotype data through lots of field investigations, used statistical methods to analyze the phenotype data, and carried out research on the identification of rice GD [13; 14; 15; 16]. The source of phenotypic data mostly relies on human experience, and the data collection process is time consuming and susceptible to environmental interference. Based on the identification results of phenotypic data, other researchers have carried out molecular QTL markers of rice variety GD to further confirm the molecules affecting rice GD [17; 18; 19; 20]. Molecular labeling methods not only require skilled operation of instruments by professionals, but also incur high costs, which limits the development of large-scale molecular detection. With the developments in science and technology, there are more and more intelligent means. It is of great significance to design and construct an efficient, non-destructive, and accurate classification and identification method for japonica rice GD. Raman spectroscopy technology can provide rapid, simple, repeatable, and non-destructive qualitative and quantitative analysis without sample preparation, which can be directly measured by laser [21]. [PERSON] [22] discussed the application of Raman spectra as an unlabeled, non-invasive, and non-destructive analysis technique for the rapid and accurate identification of nutrients in 15 different rice grains. [PERSON] et al. [23] used Raman spectroscopy to identify the varieties and origin of rice in China. [PERSON] [24] established a rapid non-destructive detection method for distinguishing rice-producing areas using Raman spectroscopy. [PERSON]'s team [25] analyzed polysaccharides from nine rice varieties in Japan based on Raman spectroscopy. At present, the application of Raman spectroscopy in the classification and identification of GD of japonica rice has not been reported.