Nowadays there are hundreds of thousands known plant species on the Earth and many are still unknown yet. The process of plant classification can be performed using different ways but the most popular approach is based on plant leaf characteristics. Most types of plants have unique leaf characteristics such as shape, color, and texture. Since machine learning and vision considerably developed in the past decade, automatic plant species (or leaf) recognition has become possible. Recently, the automated leaf classification is a standalone research area inside machine learning and several shallow and deep methods were proposed to recognize leaf types. From 2007 to present days several research papers have been published in this topic. In older studies the classifier was a shallow method while in current works many researchers applied deep networks for classification. During the overview of plant leaf classification literature, we found an interesting deficiency (lack of hyper-parameter search) and a key difference between studies (different test sets). This work gives an overall review about the efficiency of shallow and deep methods under different test conditions. It can be a basis to further research.

中文翻译：

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浅层和深度学习对植物叶片的识别：综合研究

如今，地球上有成千上万种已知的植物物种，但许多物种仍然未知。植物分类的过程可以使用不同的方法来执行，但是最流行的方法是基于植物叶片的特性。大多数类型的植物都有独特的叶子特征，例如形状，颜色和质地。由于机器学习和视觉在过去十年中得到了很大发展，因此自动识别植物（或叶子）成为可能。最近，自动叶子分类是机器学习内部的一个独立研究领域，提出了几种浅层和深层方法来识别叶子类型。从2007年至今，已经发表了有关该主题的几篇研究论文。在较早的研究中，分类器是一种浅层方法，而在当前的工作中，许多研究人员将深层网络用于分类。在植物叶片分类文献的概述中，我们发现了一个有趣的缺陷（缺少超参数搜索）和研究之间的关键区别（不同的测试集）。这项工作对浅浅和深层方法在不同测试条件下的效率进行了全面回顾。它可以作为进一步研究的基础。