Evidence map›Paper›PMID 38890375›Full record

ArticleScientific reports2024

Detecting sorghum aphid infestation in grain sorghum using leaf spectral response.

Grace Craigie, Trevor Hefley, Ivan Grijalva, Ignacio A Ciampitti, Douglas G Goodin, Brian McCornack

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

6 authors.

Grace CraigieDepartment of Entomology, Kansas State University, Manhattan, KS, 66506, USA.
Trevor HefleyDepartment of Statistics, Kansas State University, Manhattan, KS, 66506, USA.
Ivan GrijalvaDepartment of Entomology, Kansas State University, Manhattan, KS, 66506, USA. grijalva@ksu.edu.
Ignacio A CiampittiDepartment of Agronomy, Kansas State University, Manhattan, KS, 66506, USA.
Douglas G GoodinDepartment of Geography and Geospatial Sciences, Kansas State University, Manhattan, KS, 66506, USA.
Brian McCornackDepartment of Entomology, Kansas State University, Manhattan, KS, 66506, USA.

Funding

Agricultural Research Service 3072-22000-017-03-S
6 · The paper itself

Abstract

Sorghum aphid, Melanaphis sorghi (Theobald) have become a major economic pest in sorghum causing 70% yield loss without timely insecticide applications. The overarching goal is to develop a monitoring system for sorghum aphids using remote sensing technologies to detect changes in plant-aphid density interactions, thereby reducing scouting time. We studied the effect of aphid density on sorghum spectral responses near the feeding site and on distal leaves from infestation and quantified potential systemic effects to determine if aphid feeding can be detected. A leaf spectrometer at 400-1000 nm range was used to measure reflectance changes by varying levels of sorghum aphid density on lower leaves and those distant to the caged infestation. Our study results demonstrate that sorghum aphid infestation can be determined by changes in reflected light, especially between the green-red range (550-650 nm), and sorghum plants respond systemically. This study serves as an essential first step in developing more effective pest monitoring systems for sorghum aphids, as leaf reflection sensors can be used to identify aphid feeding regardless of infestation location on the plant. Future research should address whether such reflectance signatures can be detected autonomously using small unmanned aircraft systems or sUAS equipped with comparable sensor technologies.

Indexed as

AphidsPlant LeavesSorghumAnimalsRemote Sensing TechnologySpectrum Analysis

Identifiers

PMID38890375
PMCPMC11189447

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Registered trials

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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.