Research on Fine-Grained Phenotypic Analysis of Temporal Root Systems - Improved YoloV8seg Applied for Fine-Grained Analysis of In Situ Root Temporal Phenotypes.

Abstract

Root systems are crucial organs for crops to absorb water and nutrients. Conducting phenotypic analysis on roots is of great importance. To date, methods for root system phenotypic analysis have predominantly focused on semantic segmentation, integrating phenotypic extraction software to achieve comprehensive root phenotype analysis. This study demonstrates the feasibility of instance segmentation tasks on in situ root system images. An improved YoloV8n-seg network tailored for detecting elongated roots is proposed, which outperforms the original YoloV8seg in all network performance metrics. Additionally, the post-processing method introduced reduces root identification errors, ensuring a one-to-one correspondence between each root system and its detection box. The experiment yields phenotypic parameters for fine-grained roots, such as fine-grained root length, diameter, and curvature. Compared to traditional parameters like total root length and average root diameter, these detailed phenotypic analyses enable more precise phenotyping and facilitate accurate artificial intervention during crop cultivation.

Plant phenotyping relevance

根系画像から個別根の長さ・径・曲率を抽出する改良インスタンスセグメンテーション手法を開発し、性能比較と後処理も行っており、植物表現型取得が中心である。

Code and data availability

The article describes a custom cotton root image dataset (RhizoPot, Epson V39 scans, Labelme annotations) and an improved YoloV8seg pipeline with ONNX deployment, but no block contains any data or code availability statement, public repository deposit, or authors' URL for the dataset, annotations, trained model, or ONN

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