南京信息工程大学学报2025，Vol.17Issue(1)：42-52,11.DOI:10.13878/j.cnki.jnuist.20230722001
基于I_CBAM-DenseNet模型的小麦发育期识别研究
Recognition of wheat development stage based on I_CBAM-DenseNet model
摘要
Abstract
To address the low efficiency and accuracy of manual observation in recognition of crop development sta-ges,a recognition approach based on I_CBAM-DenseNet model is proposed.The approach utilizes a densely connect-ed convolutional network(DenseNet)as the backbone extraction network and incorporates a Convolutional Block Attention Module(CBAM).The Spatial Attention Module(SAM)and Channel Attention Module(CAM)in CBAM are modified from traditional serial connection to parallel connection,and the Improved CBAM(I_CBAM)is insert-ed into the last dense block of DenseNet to construct the I_CBAM-DenseNet model.Seven important development periods of wheat are selected for automatic identification.To maximize wheat feature extraction,the Excess Green(ExG)feature factor and the maximum inter-class variance method of Otsu are combined to segment the acquired wheat images.The accuracy and loss values of models including I_CBAM-DenseNet,AlexNet,ResNet,DenseNet,CBAM-DenseNet and VGG are compared and analyzed.The results show that the proposed I_CBAM-DenseNet model outperforms other models with a high accuracy of 99.64%.关键词
小麦/发育期/DenseNet/卷积块注意模块(CBAM)Key words
wheat/development stage/DenseNet/convolutional block attention module(CBAM)分类
信息技术与安全科学引用本文复制引用
付景枝,马悦,宏观,刘云平,吴文宇,丁明明,尹泽凡..基于I_CBAM-DenseNet模型的小麦发育期识别研究[J].南京信息工程大学学报,2025,17(1):42-52,11.基金项目
国家自然科学基金(51305210) （51305210）
江苏省自然科学基金(BK20150924) （BK20150924）
