Evidence map›Paper›PMID 39665152›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

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

Qiushi Yu, Meng Zhang, Liuli Wang, Xingyun Liu, Lingxiao Zhu, Liantao Liu, Nan Wang

Abstract read
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Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Qiushi YuState Key Laboratory of North China Crop Improvement and Regulation, College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071000, China.
Meng ZhangState Key Laboratory of North China Crop Improvement and Regulation, College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071000, China.
Liuli WangState Key Laboratory of North China Crop Improvement and Regulation, College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071000, China.
Xingyun LiuState Key Laboratory of North China Crop Improvement and Regulation, College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071000, China.
Lingxiao ZhuState Key Laboratory of North China Crop Improvement and Regulation, College of Agronomy, Hebei Agricultural University, Baoding, 071000, China.
Liantao LiuState Key Laboratory of North China Crop Improvement and Regulation, College of Agronomy, Hebei Agricultural University, Baoding, 071000, China.
Nan WangState Key Laboratory of North China Crop Improvement and Regulation, College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071000, China.ORCID https://orcid.org/0000-0002-4242-5555

Funding

Central Guiding Local Science and Technology Development Fund Project 236Z7402GCentral Guiding Local Science and Technology Development Fund Project 246Z7402GNational Natural Science Foundation of China 32172120National Natural Science Foundation of China 32272220State Key Laboratory of North China Crop Improvement and Regulation NCCIR2024ZZ-18S&T Program of Hebei 23567601HTop-notch Talent Plan Program of the Education Department of Hebei Province BJ2021058
6 · The paper itself

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.

Indexed as

Image Processing, Computer-AssistedPlant RootsPhenotypeSoftwarein situ root system architectureinstance segmentationphenotype analysisroot image analysis

Identifiers

PMID39665152
PMCPMC11791994

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.