LeafNet: A computer vision system for automatic plant species identification
LeafNet: A computer vision system for automatic plant species identification
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DOI：
10.1016/j.ecoinf.2017.05.005
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发表时间：
2017-07-01
影响因子：
5.1
通讯作者：
Steinhage, Volker
中科院分区：
文献类型：
--
作者：
Barre, Pierre;Stoever, Ben C.;Steinhage, Volker
目的：分类单元鉴定是许多植物生态研究的重要一步。该任务的部分自动化可能会大大提高其效率和可重复性。基于图像的识别系统是存在的，但主要依靠手工算法来提取先验选择的特征集，以识别所选分类单元的物种。因此，此类系统仅限于这些分类群，并且还需要专家参与，为开发此类定制系统提供分类学知识。本研究的目的是开发一个深度学习系统，从叶子图像中学习判别特征，以及用于植物物种识别的分类器。通过将我们的结果与 LeafSnap 等定制系统进行比较，我们可以表明，与手工制作的特征相比，通过卷积神经网络 (CNN) 学习特征可以为叶子图像提供更好的特征表示。方法：我们开发了 Lea/Net，一种基于 CNN 的植物识别系统。为了进行评估，我们利用了公开的 LeafSnap、Flavia 和 Foliage 数据集。结果：评估 LeafNet 在 LeafSnap、Flavia 和 Foliage 数据集上的识别精度，结果表明，与手工制作的定制系统相比，LeafNet 具有更好的性能。结论：考虑到植物的整体物种多样性，仅通过不断收集定制的、专业的和手工制作的（因此昂贵的）识别系统组件，不太可能实现视觉植物物种识别的完全自动化的目标。深度学习 CNN 方法提供了一种最先进的自学习替代方案，只需提供新的训练数据而不是开发新的软件系统即可适应不同的分类单元。
Aims: Taxon identification is an important step in many plant ecological studies. Its efficiency and reproducibility might greatly benefit from partly automating this task. Image-based identification systems exist, but mostly rely on hand-crafted algorithms to extract sets of features chosen a priori to identify species of selected taxa. In consequence, such systems are restricted to these taxa and additionally require involving experts that provide taxonomical knowledge for developing such customized systems. The aim of this study was to develop a deep learning system to learn discriminative features from leaf images along with a classifier for species identification of plants. By comparing our results with customized systems like LeafSnap we can show that learning the features by a convolutional neural network (CNN) can provide better feature representation for leaf images compared to hand-crafted features.Methods: We developed Lea/Net, a CNN-based plant identification system. For evaluation, we utilized the publicly available LeafSnap, Flavia and Foliage datasets.Results: Evaluating the recognition accuracies of LeafNet on the LeafSnap, Flavia and Foliage datasets reveals a better performance of LeafNet compared to hand-crafted customized systems.Conclusions: Given the overall species diversity of plants, the goal of a complete automatisation of visual plant species identification is unlikely to be met solely by continually gathering assemblies of customized, specialized and hand-crafted (and therefore expensive) identification systems. Deep Learning CNN approaches offer a self learning state-of-the-art alternative that allows adaption to different taxa just by presenting new training data instead of developing new software systems.