Rhizonet: Image Segmentation for Plant Root in Hydroponic Ecosystem

Abstract

ABSTRACT Digital cameras have the ability to capture daily images of plant roots, allowing for the estimation of root biomass. However, the complexities of root structures and noisy image backgrounds pose challenges for advanced phenotyping. Manual segmentation methods are laborious and prone to errors, which hinders experiments involving several plants. This paper introduces Rhizonet, a supervised deep learning approach for semantic segmentation of plant root images. Rhizonet harnesses a Residual U-Net backbone to enhance prediction accuracy, incorporating a convex hull operation to precisely outline the largest connected component. The primary objective is to accurately segment the biomass of the roots and analyze their growth over time. The input data comprises color images of various plant samples within a hydroponic environment known as EcoFAB, subject to specific nutrition treatments. Validation tests demonstrate the robust generalization of the model across experiments. This research pioneers advances in root segmentation and phenotype analysis by standardizing processes and facilitating the analysis of thousands of images while reducing subjectivity. The proposed root segmentation algorithms contribute significantly to the precise assessment of the dynamics of root growth under diverse plant conditions.

Plant phenotyping relevance

植物根画像から根バイオマスと成長を推定するセグメンテーション手法を開発し、実験間の汎化性能も検証しており、植物フェノタイピング手法が研究の中心です。

Code and data availability

The paper describes an EcoFAB root image dataset (643 scans, 61 annotated) and the RhizoNet model, but no public repository, deposit, or URL for the data, code, or trained weights is provided in the supplied blocks. Only contact-based availability is offered.

No evidence-backed public reproduction asset is currently recorded.