Leaf segmentation through the classification of edges

Abstract

We present an approach to leaf level segmentation of images of Arabidopsis thaliana plants based upon detected edges. We introduce a novel approach to edge classification, which forms an important part of a method to both count the leaves and establish the leaf area of a growing plant from images obtained in a high-throughput phenotyping system. Our technique uses a relatively shallow convolutional neural network to classify image edges as background, plant edge, leaf-on-leaf edge or internal leaf noise. The edges themselves were found using the Canny edge detector and the classified edges can be used with simple image processing techniques to generate a region-based segmentation in which the leaves are distinct. This approach is strong at distinguishing occluding pairs of leaves where one leaf is largely hidden, a situation which has proved troublesome for plant image analysis systems in the past. In addition, we introduce the publicly available plant image dataset that was used for this work.

Plant phenotyping relevance

葉画像から葉数・葉面積を抽出するエッジ分類および画像分割手法を開発し、ハイスループット表現型解析で評価するとともにデータセットも公開しており、植物フェノタイピング手法が中心である。

Code and data availability

The paper introduces the Aberystwyth Leaf Evaluation Dataset (ALED), a public Zenodo deposit containing the Arabidopsis top-down plant images, hand-annotated ground truth, and segmentation evaluation software used directly in this work's leaf segmentation experiments.