Zhao Xin, Zhang Jianpei, Yang Jing, Ma Bo, Liu Rui, Hu Jifang
College of Computer Science and Technology, Harbin Engineering University, Harbin 150086, China.
College of Computer and Control Engineering, Qiqihar University, Qiqihar 161006, China.
Plants (Basel). 2022 Jun 15;11(12):1573. doi: 10.3390/plants11121573.
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.
中国寒地水稻种植主要分布在黑龙江省，该地区水稻生长季易遭受低温冷害。依据积温带选择并种植适宜GD的水稻品种是预防低温冷害的重要措施。然而，传统的水稻GD鉴定方法需要大量田间调查，耗时且易受环境干扰。因此，迫切需要一种高效、准确且智能的鉴定方法。针对这一问题，我们选取了适合黑龙江省三个积温带的七个水稻品种作为研究对象，开展了基于拉曼光谱和胶囊神经网络（CapsNets）的粳稻GD鉴定研究。数据预处理阶段采用多种方法（信号滤波、差分、分割和叠加）处理拉曼光谱数据，以完成局部特征与全局特征的融合及数据维度转换。构建了一个包含三个神经元层（一个卷积层和两个胶囊层）及动态路由协议的CapsNets，并在Python中实现。在CapsNets上训练160个轮次后，该模型在训练数据集和测试数据集上的准确率分别达到89%和93%。结果表明，拉曼光谱结合CapsNets可为黑龙江省水稻GD的分类鉴定提供一种高效、准确的智能鉴定方法。
