Evidence map›Paper›PMID 37501953›Full record

ArticleHeliyon2023

Artificial neural network approach for predicting the sesame (

João Everthon da Silva Ribeiro, Ester Dos Santos Coêlho, Anna Kézia Soares de Oliveira, Antonio Gideilson Correia da Silva, Welder de Araújo Rangel Lopes, Pablo Henrique de Almeida Oliveira, Elania Freire da Silva, Aurélio Paes Barros Júnior, Lindomar Maria da Silveira

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

João Everthon da Silva RibeiroFederal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil.
Ester Dos Santos CoêlhoFederal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil.
Anna Kézia Soares de OliveiraFederal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil.
Antonio Gideilson Correia da SilvaFederal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil.
Welder de Araújo Rangel LopesFederal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil.
Pablo Henrique de Almeida OliveiraFederal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil.
Elania Freire da SilvaFederal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil.
Aurélio Paes Barros JúniorFederal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil.
Lindomar Maria da SilveiraFederal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The estimative of the leaf area using a nondestructive method is paramount for successive evaluations in the same plant with precision and speed, not requiring high-cost equipment. Thus, the objective of this work was to construct models to estimate leaf area using artificial neural network models (ANN) and regression and to compare which model is the most effective model for predicting leaf area in sesame culture. A total of 11,000 leaves of four sesame cultivars were collected. Then, the length (L) and leaf width (W), and the actual leaf area (LA) were quantified. For the ANN model, the parameters of the length and width of the leaf were used as input variables of the network, with hidden layers and leaf area as the desired output parameter. For the linear regression models, leaf dimensions were considered independent variables, and the actual leaf area was the dependent variable. The criteria for choosing the best models were: the lowest root of the mean squared error (RMSE), mean absolute error (MAE), and absolute mean percentage error (MAPE), and higher coefficients of determination (R

Indexed as

Leaf lengthLeaf widthMachine learningMultilayer perceptronsSesamum indicum L.Simple linear regression

Identifiers

PMID37501953
PMCPMC10368775

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

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.