Deep learning is currently the most important branch of machine learning, with applications in speech recognition, computer vision, image classification, and medical imaging analysis. Plant recognition is one of the areas where image classification can be used to identify plant species through their leaves. Botanists devote a significant amount of time to recognizing plant species by personally inspecting. This paper describes a method for dissecting color images of Swedish leaves and identifying plant species. To achieve higher accuracy, the task is completed using transfer learning with the help of pre-trained classifier VGG-19. The four primary processes of classification are image preprocessing, image augmentation, feature extraction, and recognition, which are performed as part of the overall model evaluation. The VGG-19 classifier grasps the characteristics of leaves by employing pre-defined hidden layers such as convolutional layers, max pooling layers, and fully connected layers, and finally uses the soft-max layer to generate a feature representation for all plant classes. The model obtains knowledge connected to aspects of the Swedish leaf dataset, which contains fifteen tree classes, and aids in predicting the proper class of an unknown plant with an accuracy of 99.70% which is higher than previous research works reported.

翻译：深层学习目前是机器学习的最重要分支,其应用包括语音识别、计算机视觉、图像分类和医学成像分析。植物识别是图像分类可用来通过叶子识别植物物种的领域之一。植物学家花大量时间亲自检查植物物种。本文描述了瑞典叶子颜色图解解剖和植物物种鉴定的方法。为了实现更高的准确性,在经过预先训练的分类师VGG-19的帮助下,通过转移学习完成这项任务。四个主要分类过程是图像预处理、图像增强、特征提取和识别,作为总体模型评估的一部分。VGG-19分类者通过使用预先界定的隐蔽层(如革命层、最大集合层和完全相连层)来掌握树叶的特性,最后使用软式成文层为所有植物类别制作特征说明。模型获得与瑞典叶数据集(包含15个树类)各方面有关的知识,并协助预测一个不为人所知的植物的适当类别,准确度为99.70%,高于以前报告的研究工作。