Leaf segmentation in plant phenotyping: a collation study
Leaf segmentation in plant phenotyping: a collation study
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DOI：
10.1007/s00138-015-0737-3
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发表时间：
2016-05-01
影响因子：
3.3
通讯作者：
Tsaftaris, Sotirios A.
中科院分区：
文献类型：
--
作者：
Scharr, Hanno;Minervini, Massimo;Tsaftaris, Sotirios A.
基于图像的植物表型分析是计算机视觉在农业中日益增长的应用领域。一个关键的任务是分割图像中的所有单个叶子。在这里，我们集中在最常见的莲座模式植物，拟南芥和年轻的烟草。虽然叶子确实共享外观和形状特征，但是叶子形状和姿态中的遮挡和可变性以及成像条件的存在使得该问题具有挑战性。本文的目的是比较几个叶分割解决方案上的一个独特的和第一的同类数据集包含图像从典型的表型实验。特别是，我们报告并讨论了2014年植物表型分析研讨会计算机视觉问题的第一个叶分割挑战的方法和结果。提出了四种方法：三个片段通过以无监督的方式处理距离变换离开，另一个片段通过最佳模板选择和倒角匹配离开。总的来说，我们发现，尽管可以以令人满意的准确度（90% Dice得分）完成将植物与背景分离，但是当叶子重叠时，单个叶子分割和计数仍然具有挑战性。此外，对于较年轻的叶子，准确性较低。我们还发现，数据集的可变性确实会影响结果。我们的研究结果激发了进一步的调查和专门的算法为这个特定的应用程序的发展，这种形式的挑战非常适合于推进最先进的数据是公开的（在线http：//www.plant-phenotyping.org/datasets），以支持未来的挑战超出分割在这个应用领域。
Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first-of-its-kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping workshop in 2014. Four methods are presented: three segment leaves by processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be accomplished with satisfactory accuracy (90 % Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Additionally, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (online at http://www.plant-phenotyping.org/datasets) to support future challenges beyond segmentation within this application domain.