Plant segmentation by supervised machine learning methods

Abstract

Abstract High‐throughput phenotyping systems provide abundant data for statistical analysis through plant imaging. Before usable data can be obtained, image processing must take place. In this study, we used supervised learning methods to segment plants from the background in such images and compared them with commonly used thresholding methods. Because obtaining accurate training data is a major obstacle to using supervised learning methods for segmentation, a novel approach to producing accurate labels was developed. We demonstrated that, with careful selection of training data through such an approach, supervised learning methods, and neural networks in particular, can outperform thresholding methods at segmentation.

Plant phenotyping relevance

植物画像から背景を分離するセグメンテーション手法を開発・比較し、教師データ生成法も提案しているため、表現型取得の技術が中心である。

Code and data availability

The paper's DATA AVAILABILITY statement points to the authors' public GitHub repository containing all segmentation analysis code and related data, and to CyVerse Data Commons hosting the raw maize image data used in the study.