江苏农业学报2025，Vol.41Issue(1)：87-100,14.DOI:10.3969/j.issn.1000-4440.2025.01.011
基于CNN和Transformer的绿豆干旱胁迫识别模型
Drought stress recognition model of mung bean based on CNN and Trans-former
摘要
Abstract
To address the issues of low recognition rate and poor timeliness in traditional methods for identifying drought stress in mung beans,this study established a mung bean drought stress recognition model named Mungbean-droughtNet based on convolutional neural network(CNN)and transformer.The model employed a dual-branch structure,utilized the global feature extraction module(GFEM)branch and the local feature extraction module(LFEM)branch to extract local and global features from the input images,respectively.Finally,multilayer perceptron(MLP)module was used to fuse the local and global features for classification.In the actual data analysis,a total of 14 536 chlorophyll fluorescence images of mung beans under drought stress were collected and classified into six catego-ries,including HR,R,MR,S,HS and the control group.The Mungbean-droughtNet model was applied to an-alyze the chlorophyll fluorescence image dataset,the re-sults showed that the Mungbean-droughtNet model achieved an average recognition accuracy of 95.57%,an average preci-sion of 98.18%,an average recall ratio of 98.40%,and an average F1-score of 98.28%for the chlorophyll fluorescence im-ages in the test set.Compared with the current advanced models EfficientNetV2 and Swin Transformer,the accuracy of the Mungbean-droughtNet model increased by 3.56 percentage points and 2.62 percentage points,respectively,demonstrating stronger robustness and better recognition performance.This study provides a foundation for research on mung bean drought stress and the excavation of drought-resistant genes.关键词
绿豆/干旱胁迫/卷积神经网络/转换器/图像识别/叶绿素荧光图像Key words
mung bean/drought stress/convolutional neural network/transformer/image recognition/chloro-phyll fluorescence image分类
农业科技引用本文复制引用
蒋东山,高尚兵,刘金洋,张浩淼,李士丛,罗仔秋,余骥远,李洁,陈新,袁星星..基于CNN和Transformer的绿豆干旱胁迫识别模型[J].江苏农业学报,2025,41(1):87-100,14.基金项目
国家食用豆产业技术体系岗位科学家项目(CARS-09-G13) （CARS-09-G13）
江苏省种业揭榜挂帅项目[JBGS(2021)004] （2021）
江苏省研究生实践创新计划项目(SJCX24_2146) （SJCX24_2146）
