Evidence map›Paper›PMID 41416180›Full record

ArticlePlant phenomics (Washington, D.C.)2025

Rapid diagnosis of herbicidal activity and mode of action using spectral image analysis and machine learning.

Tae-Kyeong Noh, Min-Jung Yook, Taek-Sung Lee, Do-Soon Kim

Abstract read
PubMed Full text at PMC Publisher
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.

  1. 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

4 authors.

Tae-Kyeong NohDepartment of Agriculture, Forestry and Bioresources, Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea.
Min-Jung YookDepartment of Agriculture, Forestry and Bioresources, Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea.
Taek-Sung LeeSmart Farm Research Center, Korea Institute of Science and Technology, Gangneung, Republic of Korea.
Do-Soon KimDepartment of Agriculture, Forestry and Bioresources, Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Herbicide screening requires a substantial amount of time, effort, and cost, making a new herbicide discovery expensive and time-consuming. Various diagnostic methods have been developed, but most of them are destructive and require significant time and effort to identify herbicide activity. Therefore, this study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs). RGB, chlorophyll fluorescence (CF), and infrared (IR) thermal images were acquired after treating herbicides with different MOAs to a model plant, oilseed rape (

Indexed as

Herbicide modes of actionHerbicide screeningMachine learningRapid diagnosisSpectral image analysis

Identifiers

PMID41416180
PMCPMC12709994

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.