基于改进VGGNet模型的外来入侵植物叶片识别方法

Leaf Recognition Method of Invasive Alien Plants Based on Improved VGGNet Model

针对自然界中不同种类植物的叶片可能存在类间差异小而导致一些边缘轮廓相似的本土植物和外来入侵植物叶片识别错误的问题,提出一种PF-VGGNet模型。常用的VGGNet模型在图像分类上表现优秀,采用顺次连接的结构,可以很好地提取图像的高级语义信息特征,但一些图像浅层的轮廓和纹理特征也对分类起到关键作用。PF-VGGNet模型可以将浅层轮廓和纹理特征与网络深层高级语义信息融合,实现对植物叶片的自动识别。实验结果表明,PF-VGGNet模型对比其它算法在自建的外来入侵植物叶片数据集上取得了较好的识别效果,在训练集和测试集上的准确率分别为99.89%和99.63%。PF-VGGNet可以有效降低因叶片边缘轮廓相近导致识别错误的问题,能够快速识别外来入侵植物叶片,为防治外来植物入侵提供支持。

In view of the leaves of different species of plants in nature may have small differences,which leads to the problem of leaf recognition errors of some native plants and invasive plants with similar edge profiles,a PF-VGGNet model is proposed.The common VGGNet model performs well in image classification.Using the sequential connection structure,it can extract the high-level semantic information features of the image,but the shallow contour and texture features of some images also play a key role in the classification.The PF-VGGNet model can fuse the shallow contour and texture features with the deep semantic information of the network to realize the automatic recognition of plant leaves.The experimental results show that the PF-VGGNet model has better recognition effect than other algorithms on the self built data set of alien invasive plant leaves,and the accuracy rates in training set and test set are 99.89%and 99.63%respectively.The PF-VGGNet can effectively reduce the problem of recognition error caused by the similar edge contour of leaves,can quickly identify the leaves of alien invasive plants,and provide support for the prevention and control of alien plants.