LeafGen: Structure-aware Leaf Image Generation for Annotation-free Leaf Instance Segmentation.

Naoki Asada, Xinpeng Liu, Kanyu Xu, Ryohei Miyakawa, Yang Yang, Hiroaki Santo, Yosuke Toda, Fumio Okura

Abstract read

In one paragraph

Article in Plant phenomics (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

ZCAT: Zero-shot cross-crop annotation transfer-A new paradigm leveraging plant organ similarity.Plant phenomics (Washington, D.C.) · 2026
Article

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

8 authors.

Naoki AsadaGraduate School of Information Science and Technology, The University of Osaka, Suita, Osaka, Japan.

Xinpeng LiuGraduate School of Information Science and Technology, The University of Osaka, Suita, Osaka, Japan.

Kanyu XuGraduate School of Information Science and Technology, The University of Osaka, Suita, Osaka, Japan.

Ryohei MiyakawaGraduate School of Information Science and Technology, The University of Osaka, Suita, Osaka, Japan.

Yang YangGraduate School of Information Science and Technology, The University of Osaka, Suita, Osaka, Japan.

Hiroaki SantoGraduate School of Information Science and Technology, The University of Osaka, Suita, Osaka, Japan.

Yosuke TodaPhytometrics, Hamamatsu, Shizuoka, Japan.

Fumio OkuraGraduate School of Information Science and Technology, The University of Osaka, Suita, Osaka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Instance segmentation of plant leaves plays a crucial role in plant phenotyping, leveraging the rapid advancements in neural network research. A significant challenge in leaf instance segmentation lies in the preparation of training datasets, which typically require manual annotations comprising numerous pairs of ground-truth masks and corresponding plant photographs. Recently, segmentation models pre-trained on large-scale datasets,

Indexed as

Data augumentationImage generationLeaf segmentation

Identifiers

PMID42041011

DOI10.1016/j.plaphe.2025.100092

PMCPMC13109295

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.