Evidence map›Paper›PMID 36236215›Full record

ArticleSensors (Basel, Switzerland)2022

Use of High-Resolution Multispectral UAVs to Calculate Projected Ground Area in

Gessica Altieri, Angela Maffia, Vittoria Pastore, Mariana Amato, Giuseppe Celano

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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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

5 authors.

Gessica AltieriSchool of Agriculture, Forestry, Food and Environmental Sciences, University of Basilicata, 85100 Potenza, Italy.
Angela MaffiaAgriculture Department, Mediterranea University, 89124 Reggio Calabria, Italy.
Vittoria PastoreAGES s.r.l., University of Basilicata, 85100 Potenza, Italy.
Mariana AmatoSchool of Agriculture, Forestry, Food and Environmental Sciences, University of Basilicata, 85100 Potenza, Italy.ORCID 0000-0003-0179-8969
Giuseppe CelanoDepartment of Pharmacy, University of Salerno, 84084 Fisciano, Italy.ORCID 0000-0002-5128-2824

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the last decade, research on Corylus avellana has focused on improving field techniques and hazelnut quality; however, climatic change and sustainability goals call for new agronomic management strategies. Precision management technologies could help improve resource use efficiency and increase grower income, but research on remote sensing systems and especially on drone devices is still limited. Therefore, the hazelnut is still linked to production techniques far from the so-called Agriculture 4.0. Unmanned aerial vehicles platforms are becoming increasingly available to satisfy the demand for rapid real-time monitoring for orchard management at spatial, spectral, and temporal resolutions, addressing the analysis of geometric traits such as canopy volume and area and vegetation indices. The objective of this study is to define a rapid procedure to calculate geometric parameters of the canopy, such as canopy area and height, by methods using NDVI and CHM values derived from UAV images. This procedure was tested on the young Corylus avellana tree to manage a hazelnut orchard in the early years of cultivation. The study area is a hazelnut orchard (6.68 ha), located in Bernalda, Italy. The survey was conducted in a six-year-old irrigated hazelnut orchard of Tonda di Giffoni and Nocchione varieties using multispectral UAV. We determined the Projected Ground Area and, on the Corylus avellana canopy trough, the vigor index NDVI (Normalized Difference Vegetation Index) and the CHM (Canopy Height Model), which were used to define the canopy and to calculate the tree crown area. The projection of the canopy area to the ground measured with NDVI values > 0.30 and NDVI values > 0.35 and compared with CHM measurements showed a statistically significant linear regression, R2 = 0.69 and R2 = 0.70, respectively. The ultra-high-resolution imagery collected with the UAV system helped identify and define each tree crown individually from the background (bare soil and grass cover). Future developments are the construction of reliable relationships between the vigor index NDVI and the Leaf Area Index (LAI), as well as the evaluation of their spatial-temporal evolution.

Indexed as

CorylusTreesPlant LeavesRemote Sensing TechnologySoilSoilCHMhazelnut treesirrigation strategiesNDVIPGAprecision agricultureUAV multispectral images

Identifiers

PMID36236215
PMCPMC9571153

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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.