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Home > Archive>Volume 17, Issue 1, 2025 >42-52. DOI:10.13878/j.cnki.jnuist.20230722001
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Recognition of wheat development stage based on I_CBAM-DenseNet model
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TP391.4

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    Abstract:

    To address the low efficiency and accuracy of manual observation in recognition of crop development stages,a recognition approach based on I_CBAM-DenseNet model is proposed.The approach utilizes a densely connected 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 inserted 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%.

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FU Jingzhi, MA Yue, HONG Guan, LIU Yunping, WU Wenyu, DING Mingming, YIN Zefan. Recognition of wheat development stage based on I_CBAM-DenseNet model[J]. Journal of Nanjing University of Information Science & Technology,2025,17(1):42-52

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  • Received:July 22,2023
  • Online: February 22,2025
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