高级检索
    戴泽翰, 郑正, 黄莉舒, 等. 基于深度卷积神经网络的柑橘黄龙病症状识别[J]. 华南农业大学学报, 2020, 41(4): 111-119. doi: 10.7671/j.issn.1001-411X.201909031
    引用本文: 戴泽翰, 郑正, 黄莉舒, 等. 基于深度卷积神经网络的柑橘黄龙病症状识别[J]. 华南农业大学学报, 2020, 41(4): 111-119. doi: 10.7671/j.issn.1001-411X.201909031
    shu
    DAI Zehan, ZHENG Zheng, HUANG Lishu, et al. Recognition of Huanglongbing symptom based on deep convolutional neural network[J]. Journal of South China Agricultural University, 2020, 41(4): 111-119. doi: 10.7671/j.issn.1001-411X.201909031
    Citation: DAI Zehan, ZHENG Zheng, HUANG Lishu, et al. Recognition of Huanglongbing symptom based on deep convolutional neural network[J]. Journal of South China Agricultural University, 2020, 41(4): 111-119. doi: 10.7671/j.issn.1001-411X.201909031
    shu

    基于深度卷积神经网络的柑橘黄龙病症状识别

    Recognition of Huanglongbing symptom based on deep convolutional neural network

      摘要
    • 摘要:
      目的  探究深度学习在柑橘 Citrus spp.黄龙病症状识别上的可行性，并评估识别器的识别准确率。
      方法  以黄龙病/非黄龙病引起的发病叶片图像及健康叶片图像为训练素材，基于卷积神经网络及迁移学习技术构建二类识别器(I-2-C和M-2-C)和八类识别器(I-8-C和M-8-C)。
      结果  M-8-C模型的整体识别表现最优，对所有图像的识别准确率为93.7%，表明构建的神经网络识别器能有效辨别柑橘黄龙病症状；I-8-C和M-8-C对所有类型图像的平均F1分值分别为77.9%和88.4%，高于I-2-C(56.3%)和M-2-C(52.5%)，表明症状细分有利于提高模型的识别能力。同时M-8-C比I-8-C略高的平均F1分值表明基于MobileNetV1结构的八类识别器识别表现略优于基于InceptionV3的八类识别器。基于M-8-C改进的识别器M-8f-C能够转移到智能手机上，在田间测试中取得较好的识别表现。
      结论  基于深度学习和迁移学习开发的识别器对黄龙病单叶症状具有较好的识别效果。

       

      Abstract:
      Objective  To explore the capability of deploying deep learning to the detection of Huanglongbing (HLB) symptom inCitrus spp., and evaluate the classification accuracies of the classifiers.
      Method  Two-class classifiers(I-2-C and M-2-C) and eight-class classifiers(I-8-C and M-8-C) were constructed using images of diseased leaves caused by HLB/non-HLB and healthy leaves based on convolutional neural networks and transfer learning.
      Result  The overall classification performance of M-8-C stood out in all classifiers with accuracy of 93.7%, implying great capability in deep convolutional neural networks for classifying HLB symptoms. The mean F1 socres of I-8-C and M-8-C were 77.9% and 88.4% respectively, which were higher than those of I-2-C(56.3%) and M-2-C(52.5%). This indicated that subtyping symptoms could help improve the recognition ability of models. The slightly higher mean F1 score of M-8-C compared with I-8-C indicated that the eight-class model based on MobileNetV1 had better performance than the one based on InceptionV3. An optimized model, namely M-8f-C, was developed based on M-8-C and was successfully mounted on mobile phone. The field tests showed that M-8f-C was of decent performance under field conditions.
      Conclusion  Classifier based on deep learning and transfer learning has high accuracy for recognizing HLB symptom leaves.

       

      • HTML全文
      • 参考文献(26)
      • 相关文章
      • 施引文献
    • 资源附件(0)

    /

    返回文章
    返回