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

Abstract

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.

Plant phenotyping relevance

ラマン分光とCapsNetを組み合わせ、イネの生育期間という植物形質を推定・分類する手法を開発し、学習・テスト精度で評価しているため、表現型取得・抽出法が中心です。

Code and data availability

The paper's Data Availability Statement explicitly states that the study's data (Raman spectral data of 245 japonica rice grain samples) and code (spectral preprocessing and CapsNets classification) are openly available in the authors' public GitHub repository, which is listed in the allowed URLs.