LMS-Net: A Light-Weight Network for Mungbean Salt Stress Identification

文献类型： 外文期刊

第一作者： Zhang, Haomiao

作者： Zhang, Haomiao;Jiang, Dongshan;Luo, Ziqiu;Liang, Wenxiang;Gao, Shangbing;Zhang, Haomiao;Liu, Jinyang;Jiang, Dongshan;Luo, Ziqiu;Liang, Wenxiang;Li, Shicong;Chen, Xin;Yuan, Xingxing

作者机构：

关键词： Fluorescence; Imaging; Computational modeling; Convolutional neural networks; Crops; Computer architecture; Salt; Mungbean salt stress; salt tolerance; chlorophyll fluorescence imaging; lightweight CNN; structural reparameterization; attention mechanism

期刊名称：IEEE ACCESS （ 影响因子：3.6； 五年影响因子：3.9 ）

ISSN： 2169-3536

年卷期： 2024 年 12 卷

页码：

收录情况： SCI

摘要： Salt stress is one of the main factors affecting mungbean production, and the assessment of salt tolerance in mungbean typically requires long-term morphological observation or time-consuming physiological and biochemical experiments, which are time-consuming and labor-intensive in field conditions. This study introduced a salt stress level identification model for mungbeans, named LMS-Net, which used early-stage chlorophyll fluorescence imaging and a lightweight convolutional neural network (CNN) model to identify the salt tolerance of seedlings. To facilitate deployment on mobile and portable devices, we incorporated depth-wise separable convolutions and reparameterization techniques into LMS-Net to reduce latency during inference. Moreover, to better capture the key features and regions of stress, we proposed a Multi-scale Collaborative Attention Mechanism (MSCA) into LMS-Net. Using the FluorCam Open Chlorophyll Fluorescence Imaging System, we collected 1,879 chlorophyll fluorescence images of mungbean natural populations under salt stress to create a mungbean salt stress chlorophyll fluorescence dataset. Through comprehensive comparisons with mainstream lightweight models based on CNN and Transformer architectures, the LMS-Net model achieved an average identification accuracy of 97.64%, an average precision of 97.78%, an average recall of 97.74%, and an average F1-score of 97.75% on the test set, which benefit from model architecture and MSCA. These classification performance metrics surpass those of the comparative models. Using this method, we identified two highly salt-tolerant mungbean materials, 'Suhuang 1' and 'Wei 9002-341', from a natural population of 230 mungbean accessions. In summary, this method is promising and valuable for early-stage salt tolerance assessment of mungbean seedlings, offering new insights for screening salt-tolerant germplasm resources in mungbeans and other crops.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序