Plant species recognition is a critical step in protecting plant diversity. Leaf-based plant species recognition research is important and challenging due to the large within-class difference and between-class similarity of leaves and the rich inconsistent leaves with different sizes, colors, shapes, textures, and venations. Most existing plant leaf recognition methods typically normalize all leaf images to the same size and then recognize them at one scale, which results in unsatisfactory performances. A novel multiscale convolutional neural network with attention (AMSCNN) model is constructed for plant species recognition. In AMSCNN, multiscale convolution is used to learn the low-frequency and high-frequency features of the input images, and an attention mechanism is utilized to capture rich contextual relationships for better feature extraction and improving network training. Extensive experiments on the plant leaf dataset demonstrate the remarkable performance of AMSCNN compared with the hand-crafted feature-based methods and deep-neural network-based methods. The maximum accuracy attained along with AMSCNN is 95.28%.

中文翻译：

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用于植物物种识别的多尺度卷积神经网络

植物物种识别是保护植物多样性的关键步骤。由于叶子的类内差异和类间相似性很大，并且具有不同大小、颜色、形状、纹理和脉络的丰富不一致的叶子，因此基于叶子的植物物种识别研究是重要且具有挑战性的。大多数现有的植物叶片识别方法通常将所有叶片图像归一化为相同大小，然后在一个尺度上识别它们，这导致性能不令人满意。构建了一种新的多尺度卷积神经网络注意（AMSCNN）模型用于植物物种识别。在AMSCNN中，使用多尺度卷积来学习输入图像的低频和高频特征，并且利用注意力机制来捕获丰富的上下文关系，以更好地提取特征并改进网络训练。与手工制作的基于特征的方法和基于深度神经网络的方法相比，对植物叶子数据集的大量实验证明了 AMSCNN 的显着性能。与 AMSCNN 一起获得的最大准确率为 95.28%。