A Review of Deep Learning in The Field of Plant Root Segmentation

Abstract

Plant root segmentation is an important research task, which is of great significance for understanding plant growth and development process. Deep learning has become a research direction worthy of attention in this field. This paper mainly introduces plant root segmentation methods based on deep learning, and reviews the application of various methods in different fields. The problems of data quality, model fitting ability and real-time performance, and the significance of transfer learning, multi-task learning and reinforcement learning in application are put forward. Finally, it is pointed out that future research should focus on how to better cope with the challenges of root morphology and scale change, and pay more attention to the robustness and scalability of the algorithm. In conclusion, deep learning has had an important impact on image segmentation of plant roots.

Plant phenotyping relevance

植物根の画像セグメンテーション手法を深層学習の観点から体系的にレビューしており、根形態の抽出というフェノタイピング手法が中心です。

Code and data availability

This is a review article on deep learning for plant root segmentation. It cites prior studies (SegRoot, RootNav 2.0, GT-RootS, etc.) but provides no paper-specific public datasets, images, code, models, or supplements with availability statements. No allowed URLs are provided, and no author-deposited assets are present

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