Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.

Abstract

Cosegmentation is a newly emerging computer vision technique used to segment an object from the background by processing multiple images at the same time. Traditional plant phenotyping analysis uses thresholding segmentation methods which result in high segmentation accuracy. Although there are proposed machine learning and deep learning algorithms for plant segmentation, predictions rely on the specific features being present in the training set. The need for a multi-featured dataset and analytics for cosegmentation becomes critical to better understand and predict plants' responses to the environment. High-throughput phenotyping produces an abundance of data that can be leveraged to improve segmentation accuracy and plant phenotyping. This paper introduces four datasets consisting of two plant species, Buckwheat and Sunflower, each split into control and drought conditions. Each dataset has three modalities (Fluorescence, Infrared, and Visible) with 7 to 14 temporal images that are collected in a high-throughput facility at the University of Nebraska-Lincoln. The four datasets (which will be collected under the CosegPP data repository in this paper) are evaluated using three cosegmentation algorithms: Markov random fields-based, Clustering-based, and Deep learning-based cosegmentation, and one commonly used segmentation approach in plant phenotyping. The integration of CosegPP with advanced cosegmentation methods will be the latest benchmark in comparing segmentation accuracy and finding areas of improvement for cosegmentation methodology.

Plant phenotyping relevance

植物フェノタイピング用のマルチモーダル・時系列データセットを開発し、複数のコセグメンテーション手法をベンチマークする研究であり、画像からの植物抽出・表現型解析手法が中心です。

Code and data availability

The paper's CosegPP plant image dataset (Buckwheat/Sunflower, multi-modal, with ground-truth masks) is publicly deposited on Zenodo per the Data Availability statement. The GitHub repos mentioned (MIG, Subdiscover, DeepCO3) are cited third-party prior-work code, not authors' paper-specific analysis code.