VT-SCC: Visual-text semantic alignment via multimodal guidance for unified staple crop organ counting.

Lanying Wang, Zhengbo Liu, Xinyu Dong, Hainan Wang, Wei Guo, Yuanyuan Xiao, Qi Wang

Abstract read

In one paragraph

Article in aBIOTECH, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Lanying WangState Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.

Zhengbo LiuSchool of Integrated Circuit Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.

Xinyu DongState Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.

Hainan WangSchool of Mechanical Engineering, Guiyang University, Guiyang, 550005, China.

Wei GuoGraduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishitokyo City, Tokyo, 188-0002, Japan.

Yuanyuan XiaoState Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.

Qi WangState Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate counting of yield-related plant organs is fundamental to crop breeding and management. However, scalable counting remains challenging because crop morphology varies widely and field environments are complex. Current specialized models lack flexibility, whereas generic few-shot counters often exhibit feature ambiguity arising from their reliance on visual exemplars, leading to frequent misidentification in dense scenes. To address these challenges, we developed VT-SCC, a visual-text unified staple crop organ counting framework that incorporates semantic guidance into visual representation learning. VT-SCC integrates a Scale-Adaptive Feature Aggregator (SAFA), which uses a multi-branch structure to adaptively capture targets with large size variation, and a Semantic-Guided Channel Modulator (SGCM), which uses category text prompts to calibrate visual features dynamically and suppress background interference. In extensive experiments on a diverse dataset comprising wheat (

Indexed as

Agricultural visionCrop organ countingHigh-throughput phenotypingMultimodal prompts

Identifiers

PMID42729244

DOI10.1016/j.abiote.2026.100082

PMCPMC13561956

What OpenQuestion holds

Textmetadata

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.