Background Automated species identification is a long term research subject. Contrary to flowers and fruits, leaves are available throughout most of the year. Offering margin and texture to characterize a species, they are the most studied organ for automated identification. Substantially matured machine learning techniques generate the need for more training data (aka leaf images). Researchers as well as enthusiasts miss guidance on how to acquire suitable training images in an efficient way. Methods In this paper, we systematically study nine image types and three preprocessing strategies. Image types vary in terms of in-situ image recording conditions: perspective, illumination, and background, while the preprocessing strategies compare non-preprocessed, cropped, and segmented images to each other. Per image type-preprocessing combination, we also quantify the manual effort required for their implementation. We extract image features using a convolutional neural network, classify species using the resulting feature vectors and discuss classification accuracy in relation to the required effort per combination. Results The most effective, non-destructive way to record herbaceous leaves is to take an image of the leaf's top side. We yield the highest classification accuracy using destructive back light images, i.e., holding the plucked leaf against the sky for image acquisition. Cropping the image to the leaf's boundary substantially improves accuracy, while precise segmentation yields similar accuracy at a substantially higher effort. The permanent use or disuse of a flash light has negligible effects. Imaging the typically stronger textured backside of a leaf does not result in higher accuracy, but notably increases the acquisition cost. Conclusions In conclusion, the way in which leaf images are acquired and preprocessed does have a substantial effect on the accuracy of the classifier trained on them. For the first time, this study provides a systematic guideline allowing researchers to spend available acquisition resources wisely while yielding the optimal classification accuracy.

中文翻译：

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获取和预处理叶片图像以进行自动植物识别：了解工作量和信息增益之间的权衡。

背景 自动物种识别是一个长期的研究课题。与鲜花和水果相反，一年中的大部分时间都有叶子。提供边缘和纹理来表征物种，它们是用于自动识别的研究最多的器官。相当成熟的机器学习技术产生了对更多训练数据（又名叶子图像）的需求。研究人员和爱好者都错过了有关如何以有效方式获取合适训练图像的指导。方法在本文中，我们系统地研究了九种图像类型和三种预处理策略。图像类型在原位图像记录条件方面有所不同：透视、光照和背景，而预处理策略将未预处理、裁剪和分割的图像相互比较。每个图像类型预处理组合，我们还量化了实施它们所需的手动工作量。我们使用卷积神经网络提取图像特征，使用生成的特征向量对物种进行分类，并讨论与每个组合所需的努力相关的分类准确性。结果 记录草本叶子的最有效、无损的方法是拍摄叶子顶部的图像。我们使用破坏性背光图像产生最高的分类精度，即将采摘的叶子对着天空进行图像采集。将图像裁剪到叶子的边界大大提高了准确性，而精确的分割以更高的工作量产生了相似的准确性。永久使用或不使用闪光灯的影响可以忽略不计。对叶子的通常具有更强纹理的背面进行成像不会导致更高的准确性，但会显着增加采集成本。结论 总之，获取和预处理叶子图像的方式确实对训练在它们上的分类器的准确性有很大影响。这项研究首次提供了一个系统的指导方针，使研究人员能够明智地使用可用的采集资源，同时产生最佳的分类精度。