Evidence map›Paper›PMID 31889984›Full record

ArticlePlant methods2019

An efficient RGB-UAV-based platform for field almond tree phenotyping: 3-D architecture and flowering traits.

Francisca López-Granados, Jorge Torres-Sánchez, Francisco M Jiménez-Brenes, Octavio Arquero, María Lovera, Ana I de Castro

Open access · goldAbstract read
In one paragraph

Article in Plant methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.7field-weighted citation impact, top 7% of its field
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

11 citing papers in PubMed, 69 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Francisca López-Granados1Institute for Sustainable Agriculture IAS-CSIC, 14004 Córdoba, Spain.
Jorge Torres-Sánchez1Institute for Sustainable Agriculture IAS-CSIC, 14004 Córdoba, Spain.
Francisco M Jiménez-Brenes1Institute for Sustainable Agriculture IAS-CSIC, 14004 Córdoba, Spain.
Octavio Arquero2Institute of Agricultural Research and Training (IFAPA-Alameda del Obispo), 14004 Córdoba, Spain.
María Lovera2Institute of Agricultural Research and Training (IFAPA-Alameda del Obispo), 14004 Córdoba, Spain.
Ana I de Castro1Institute for Sustainable Agriculture IAS-CSIC, 14004 Córdoba, Spain.ORCID 0000-0002-6699-2204
Instituto de Agricultura Sostenible · ESAndalusian Institute of Agricultural and Fisheries Research and Training · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlmond is an emerging crop due to the health benefits of almond consumption including nutritional, anti-inflammatory, and hypocholesterolaemia properties. Traditional almond producers were concentrated in California, Australia, and Mediterranean countries. However, almond is currently present in more than 50 countries due to breeding programs have modernized almond orchards by developing new varieties with improved traits related to late flowering (to reduce the risk of damage caused by late frosts) and tree architecture. Almond tree architecture and flowering are acquired and evaluated through intensive field labour for breeders. Flowering detection has traditionally been a very challenging objective. To our knowledge, there is no published information about monitoring of the tree flowering dynamics of a crop at the field scale by using color information from photogrammetric 3D point clouds and OBIA. As an alternative, a procedure based on the generation of colored photogrammetric point clouds using a low cost (RGB) camera on-board an unmanned aerial vehicle (UAV), and an semi-automatic object based image analysis (OBIA) algorithm was created for monitoring the flower density and flowering period of every almond tree in the framework of two almond phenotypic trials with different planting dates.

resultsOur method was useful for detecting the phenotypic variability of every almond variety by mapping and quantifying every tree height and volume as well as the flowering dynamics and flower density. There was a high level of agreement among the tree height, flower density, and blooming calendar derived from our procedure on both fields with the ones created from on-ground measured data. Some of the almond varieties showed a significant linear fit between its crown volume and their yield.

conclusionsOur findings could help breeders and researchers to reduce the gap between phenomics and genomics by generating accurate almond tree information in an efficient, non-destructive, and inexpensive way. The method described is also useful for data mining to select the most promising accessions, making it possible to assess specific multi-criteria ranking varieties, which are one of the main tools for breeders.

Indexed as

Colored point cloudsLow-cost cameraObject-based image analysis (OBIA)Remote sensingVolume and 3D mappingWoody crop

Identifiers

PMID31889984
PMCPMC6931260
OpenAlexW2998304066

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.