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首页> 外文期刊>Journal of Imaging Science and Technology >Apple Leaf Disease Identification Through Region-of-Interest-Aware Deep Convolutional Neural Network
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Apple Leaf Disease Identification Through Region-of-Interest-Aware Deep Convolutional Neural Network

机译：苹果叶疾病识别通过感兴趣的区域感知深卷积神经网络

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摘要

Traditional approaches for the identification of leaf diseases involve the use of handcrafted features such as colors and textures for feature extraction. Therefore, these approaches may have limitations in extracting abundant and discriminative features. Although deep learning approaches have been recently introduced to overcome the shortcomings of traditional approaches, existing deep learning models such as VGG and ResNet have been used in these approaches. This indicates that the approach can be further improved to increase the discriminative power because the spatial attention mechanism to predict the background and spot areas (i.e., local areas with leaf diseases) has not been considered. Therefore, a new deep learning architecture, which is hereafter referred to as region-of-interest-aware deep convolutional neural network (ROI-aware DCNN), is proposed to make deep features more discriminative and increase classification performance. The primary idea is that leaf disease symptoms appear in leaf area, whereas the background region does not contain useful information regarding leaf diseases. To realize this, two subnetworks are designed. One subnetwork is the ROI subnetwork to provide more discriminative features from the background, leaf areas, and spot areas in the feature map. The other subnetwork is the classification subnetwork to increase the classification accuracy. To train the ROI-aware DCNN, the ROI subnetwork is first learned with a new image set containing the ground truth images where the background, leaf area, and spot area are divided. Subsequently, the entire network is trained in an end-to-end manner to connect the ROI subnetwork with the classification subnetwork through a concatenation layer. The experimental results confirm that the proposed ROI-aware DCNN can increase the discriminative power by predicting the areas in the feature map that are more important for leaf diseases identification. The results prove that the proposed method surpasses conventional state-of-the-art methods such as VGG, ResNet, SqueezeNet, bilinear model, and multiscale-based deep feature extraction and pooling. (C) 2020 Society for Imaging Science and Technology.
机译：鉴定叶片疾病的传统方法涉及使用手工制作的功能，例如颜色和纹理进行特征提取。因此，这些方法可能具有提取丰富和辨别特征的局限性。虽然最近引入了深度学习方法以克服传统方法的缺点，但在这些方法中使用了现有的深度学习模型，如VGG和RENET。这表明可以进一步改善该方法以增加鉴别的动力，因为不考虑空间注意机制（即，具有叶片疾病的局部区域）。因此，提出了一种新的深度学习架构，下文称为感兴趣的深度卷积神经网络（ROI-Aware DCNN），以使深入特征更加辨别和增加分类性能。主要思想是叶片症状出现在叶面积中，而背景区域不含有关叶片疾病的有用信息。为了实现这一点，设计了两个子网。一个子网是ROI子网，以提供来自特征图中的背景，叶区域和点区域的更多辨别特征。另一个子网是分类子网，以提高分类准确性。为了训练ROI感知DCNN，首先使用包含背景，叶面积和斑点区域的地面真实图像的新图像集进行ROI子网。随后，整个网络以端到端的方式训练，以通过级联层将ROI子网与分类子网连接。实验结果证实，所提出的ROI感知DCNN可以通过预测叶片疾病更重要的特征图中的区域来增加辨别力。结果证明，该方法超越了传统的现有方法，如VGG，Reset，挤压，双线性模型和多尺度的深色特征提取和汇集。 （c）2020年影像科技协会。

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  • 来源
    《Journal of Imaging Science and Technology》 |2020年第2期|020507.1-020507.10|共10页
  • 作者

    Yu Hee-Jin; Son Chang-Hwan; Lee Dong Hyuk;

  • 作者单位

    Kunsan Natl Univ Dept Software Convergence Engn 558 Daehak Ro Gunsan Si 54150 South Korea;

    Kunsan Natl Univ Dept Software Convergence Engn 558 Daehak Ro Gunsan Si 54150 South Korea;

    Natl Inst Hort & Herbal Sci Apple Res Inst Gunwi 39000 South Korea;

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