Evidence map›Paper›PMID 29977571›Full record

ArticleHorticulture research2018

Application of light detection and ranging and ultrasonic sensors to high-throughput phenotyping and precision horticulture: current status and challenges.

André F Colaço, José P Molin, Joan R Rosell-Polo, Alexandre Escolà

Abstract read
In one paragraph

Article in Horticulture research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 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

4 authors.

André F Colaço1Biosystems Engineering Department, "Luiz de Queiroz" College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, Piracicaba - SP 13418-900 Brazil.ORCID 0000-0003-1741-9054
José P Molin1Biosystems Engineering Department, "Luiz de Queiroz" College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, Piracicaba - SP 13418-900 Brazil.
Joan R Rosell-Polo2Research Group on AgroICT and Precision Agriculture, Department of Agricultural and Forest Engineering, School of Agrifood and Forestry Science and Engineering, University of Lleida - Agrotecnio Center, Av. Rovira Roure, 191, 25198 Lleida, Catalonia Spain.ORCID 0000-0001-6746-2830
Alexandre Escolà2Research Group on AgroICT and Precision Agriculture, Department of Agricultural and Forest Engineering, School of Agrifood and Forestry Science and Engineering, University of Lleida - Agrotecnio Center, Av. Rovira Roure, 191, 25198 Lleida, Catalonia Spain.ORCID 0000-0002-9775-5471

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ultrasonic and light detection and ranging (LiDAR) sensors have been some of the most deeply investigated sensing technologies within the scope of digital horticulture. They can accurately estimate geometrical and structural parameters of the tree canopies providing input information for high-throughput phenotyping and precision horticulture. A review was conducted in order to describe how these technologies evolved and identify the main investigated topics, applications, and key points for future investigations in horticulture science. Most research efforts have been focused on the development of data acquisition systems, data processing, and high-resolution 3D modeling to derive structural tree parameters such as canopy volume and leaf area. Reported applications of such sensors for precision horticulture were restricted to real-time variable-rate solutions where ultrasonic or LiDAR sensors were tested to adjust plant protection product or fertilizer dose rates according to the tree volume variability. More studies exploring other applications in site-specific management are encouraged; some that integrates canopy sensing data with other sources of information collected at the within-grove scale (e.g., digital elevation models, soil type maps, historical yield maps, etc.). Highly accurate 3D tree models derived from LiDAR scanning demonstrate their great potential for tree phenotyping. However, the technology has not been widely adopted by researchers to evaluate the performance of new plant varieties or the outcomes from different management practices. Commercial solutions for tree scanning of whole groves, orchards, and nurseries would promote such adoption and facilitate more applied research in plant phenotyping and precision horticulture.

Identifiers

PMID29977571
PMCPMC6026496

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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.