SAM-GAN: An improved DCGAN for rice seed viability determination using near-infrared hyperspectral imaging

文献类型： 外文期刊

第一作者： Qi, Hengnian

作者： Qi, Hengnian;Huang, Zihong;Jin, Baichuan;Tang, Qizhe;Jia, Liangquan;Sun, Zeyu;Zhang, Chu;Zhao, Guangwu;Cao, Dongdong

作者机构：

关键词： Near-infrared hyperspectral imaging; Seed viability; Spectral Angle Mapper GAN; Data augmentation; Convolutional Neural Network

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2024 年 216 卷

页码：

收录情况： SCI

摘要： Viability is a significant indicator of rice seeds, affecting rice yield and quality. Existing viability determination methods cannot meet the requirements of rapidity, non-destructive and accuracy. In this study, near-infrared hyperspectral imaging was used to detect the viability of natural aging seeds. Generative Adversarial Network (GAN) is the main means of coping with Few-shot learning. Considering that natural aging seed samples were difficult to obtain and the number was scarce, this study used Spectral Angle Mapper Generative Adversarial Network (SAM-GAN) to generate rice seed spectral data based on the spectra of obtained natural aging seeds to solve the problem of sample scarcity. SAM-GAN is based on Deep Convolution GAN (DCGAN), introduced by SAM. SAM-GAN was compared with Wasserstein Generative Adversarial Nets with Gradient Penalty (WGAN-GP) and DCGAN, and the Convolutional Neural Network (CNN) model was established by three modeling methods: real data modeling, fake data modeling and mixed modeling of real data and fake data. The experimental results show that the accuracy of the CNN model established by mixing real data with fake data generated by SAM-GAN reaches nearly 100%. This study provides an effective method for rapid, non-destructive and accurate determination of rice seed viability with a limited sample number.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序