Evidence map›Paper›PMID 39205020›Full record

ArticleSensors (Basel, Switzerland)2024

Evaluating UAV-Based Remote Sensing for Hay Yield Estimation.

Kyuho Lee, Kenneth A Sudduth, Jianfeng Zhou

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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
  2. Article
  3. 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

3 authors.

Kyuho LeeDepartment of Chemical and Biomedical Engineering, University of Missouri, Columbia, MO 65211, USA.ORCID 0000-0002-3186-8975
Kenneth A SudduthUSDA-ARS Cropping Systems and Water Quality Research Unit, Columbia, MO 65211, USA.ORCID 0000-0002-2558-0668
Jianfeng ZhouDivision of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA.ORCID 0000-0002-7127-1428

Funding

Agricultural Research Service 5070-12000-001-004S
6 · The paper itself

Abstract

(1) Background: Yield-monitoring systems are widely used in grain crops but are less advanced for hay and forage. Current commercial systems are generally limited to weighing individual bales, limiting the spatial resolution of maps of hay yield. This study evaluated an Uncrewed Aerial Vehicle (UAV)-based imaging system to estimate hay yield. (2) Methods: Data were collected from three 0.4 ha plots and a 35 ha hay field of red clover and timothy grass in September 2020. A multispectral camera on the UAV captured images at 30 m (20 mm pixel

Indexed as

BiomassRemote Sensing TechnologyCrops, AgriculturalUnmanned Aerial Deviceshay yield-monitoring systemmultispectral imageprecision agricultureremote-sensing technologyUAV

Identifiers

PMID39205020
PMCPMC11360442

What OpenQuestion holds

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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.