Early and accurate assessment of plant disease severity is key to preventing disease attack. Traditional detection methods rely on manual vision to distinguish between types of disease infection, but this is time consuming, laborious and inaccurate. To address this problem, this paper proposes a deep learning‐based attentional network model (SEV‐Net) for plant disease severity identification and classification. The network embeds the improved channel and spatial attention module into the residual block of ResNet. The proposed attention module reduces the redundancy of information between channels and focuses on the most information‐rich regions of the feature map. In this experiment, SEV‐Net achieved an accuracy of 97.59% and 95.37% for multiple and single plant (Tomato) disease severity classification, which was better than existing attentional networks (SE‐Net and CBAM). Moreover, the combination of visualization techniques showed that SEV‐Net was adept at distinguishing small variations between plant diseases, proving the feasibility and effectiveness of the network. Furthermore, we have also designed and developed an Android application for real‐time classification of plant disease severity. The system deploys the SEV‐Net network model, which has higher classification accuracy and faster recognition speed.

中文翻译：

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SEV-Net：嵌入了注意力机制的残留网络，用于植物病害严重性检测

尽早而准确地评估植物病害的严重程度是预防疾病发作的关键。传统的检测方法依靠人工视觉来区分疾病感染的类型，但这是费时，费力且不准确的。为了解决这个问题，本文提出了一种基于深度学习的注意力网络模型（SEV-Net），用于植物病害严重性的识别和分类。该网络将改进的频道和空间关注模块嵌入到ResNet的剩余块中。所提出的注意力模块减少了通道之间信息的冗余，并专注于特征图上信息最丰富的区域。在此实验中，SEV-Net对多株和单株（番茄）病害严重程度分类的准确度为97.59％和95.37％，这比现有的关注网络（SE-Net和CBAM）要好。此外，可视化技术的结合表明SEV-Net善于区分植物病害之间的细微差异，证明了该网络的可行性和有效性。此外，我们还设计并开发了一个Android应用程序，用于对植物病害严重程度进行实时分类。系统部署了SEV-Net网络模型，该模型具有更高的分类准确度和更快的识别速度。我们还设计并开发了一个Android应用程序，用于对植物病害严重程度进行实时分类。系统部署了SEV-Net网络模型，该模型具有更高的分类准确度和更快的识别速度。我们还设计并开发了一个Android应用程序，用于对植物病害严重程度进行实时分类。系统部署了SEV-Net网络模型，该模型具有更高的分类精度和更快的识别速度。