Identifying plants is not only the job of professionals, but also useful or essential for the plant lover and the general public. Although deep learning approaches for plant recognition are promising, driven by the success of convolutional neural networks (CNN), their performances are still far from the requirements of an in-field scenario. First, we propose a central attention concept that helps focus on the target instead of backgrounds in the image for tree species recognition. It could prevent model training from confused vision by establishing a dual path CNN deep learning framework, in which the central attention model combined with the CNN model based on InceptionV3 were employed to automatically extract the features. These two models were then learned together with a shared classification layer. Experimental results assessed the effectiveness of our proposed approach which outperformed each uni-path alone, and existing methods in the whole plant recognition system. Additionally, we created our own tree image database where each photo contained a wealth of information on the entire tree instead of an individual plant organ. Lastly, we developed a prototype system of an online/offline available tree species identification working on a consumer mobile platform that can identify the tree species not only by image recognition, but also detection and classification in real-time remotely.

中文翻译：

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真实世界树种识别中的集中注意力和双路径卷积神经网络

识别植物不仅是专业人员的工作，而且对植物爱好者和公众也有用或必不可少。尽管在卷积神经网络（CNN）成功的推动下，用于植物识别的深度学习方法很有前途，但是它们的性能仍远非现场场景的要求。首先，我们提出一个集中注意的概念，该概念有助于将注意力集中在目标上，而不是图像中的背景，以进行树种识别。它可以通过建立双路径CNN深度学习框架来防止混淆模型训练，该模型采用集中注意力模型与基于InceptionV3的CNN模型相结合来自动提取特征。然后将这两个模型与一个共享的分类层一起学习。实验结果评估了我们提出的方法的有效性，该方法优于单独的每个单路径，以及整个植物识别系统中的现有方法。此外，我们创建了自己的树木图像数据库，其中每张照片都包含整个树木的信息，而不是单个植物器官的信息。最后，我们开发了一种在消费者移动平台上工作的在线/离线可用树种识别的原型系统，该系统不仅可以通过图像识别来识别树种，还可以实时进行远程检测和分类。我们创建了自己的树木图像数据库，其中每张照片都包含有关整个树木而不是单个植物器官的大量信息。最后，我们开发了一种在消费者移动平台上工作的在线/离线可用树种识别的原型系统，该系统不仅可以通过图像识别来识别树种，还可以实时进行远程检测和分类。我们创建了自己的树木图像数据库，其中每张照片都包含有关整个树木而不是单个植物器官的大量信息。最后，我们开发了一种在消费者移动平台上工作的在线/离线可用树种识别的原型系统，该系统不仅可以通过图像识别来识别树种，还可以实时进行远程检测和分类。