Leaf segmentation in plant phenotyping: a collation study

Abstract

Image-based plant phenotyping is a growing application domain 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 (LSC) of the Computer Vision Problems in Plant Phenotyping (CVPPP) workshop in 2014. Four methods are presented: three segment leaves via 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 achieved with satisfactory accuracy (>90% Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Besides, 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 (http://www.plantphenotyping.org/CVPPP2014-dataset) to support future challenges beyond segmentation within this application domain.

A cognitive architecture for automatic gardening

“…Segmentation and counting of leaves in images is a hard problem and still an ongoing research topic [25]. Tobacco images acquired in our scenario as well as Arabidopsis images, both of them accompanied by ground truth segmentations and annotations, are available to the public as a benchmark dataset [9].…”

Machine Learning for Plant Phenotyping Needs Image Processing

“…To offer an example, Figure 1(D) illustrates the task of segmenting individual plant leaves [7] for estimating per-leaf growth (when this task is repeated in a longitudinal fashion [8]). Here occlusion and lack of discernible boundaries (edges) between leaves make the segmentation task difficult and additional information (e.g., depth) may be required.…”

“…A recent special issue on Computer Vision and Image Analysis in Plant Phenotyping provided a good summary of the advances that occurred based on these efforts [9]. These workshops also served as the hosting venue to image-based phenotyping challenges 4 , which led to a summarizing collation study [7].…”

Special issue on computer vision and image analysis in plant phenotyping

“…Six of them [1,3,11,13,18,21] were developed from conference contributions at CVPPP 2014 [2,4,12,14,19,20]. Two other papers received by the open call already appeared in a preceding or an adjacent issue of this journal and are also part of this special issue [9,22].…”

“…A survey on results of the Leaf Segmentation Challenge at CVPPP 2014 presents state-of-the-art algorithms competing on the challenging problem of segmenting each single leaf from images showing rosette plants [22] on the basis of a recently published benchmark dataset [15]. This was the first serious computer vision dataset within the plant domain.…”