Investigation of factors affecting fresh herbage yield in pea (Pisum arvense L.) using data mining algorithms

This study was carried out to determine the factors affecting the wet grass yield of pea plants grown in Turkey. Wet grass yield was predicted using parameters such as genotype, crude protein, crude ash, acid detergent fiber (ADF), and neutral detergent fiber (NDF) with some data mining algorithms....

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Frontiers in plant science Jg. 15; S. 1482723
Hauptverfasser: Çatal, Muhammed İkbal, Çelik, Şenol, Bakoğlu, Adil
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media SA 20.11.2024
Frontiers Media S.A
Schlagworte:
Agricultural production
Algorithms
ANN
Artificial neural networks
CART
CHAID
Chi-square test
Classification
Criteria
Crop rotation
Data mining
Decision trees
Genotypes
Goodness of fit
Grasses
Legumes
MARS algorithm
Multivariate analysis
Neural networks
Nitrogen
PEA
Peas
Plant Science
Proteins
Regression analysis
Root-mean-square errors
Seeds
Soil sciences
Variables
Europe
ANN
MARS algorithm
PEA
CHAID
CART
ISSN:1664-462X, 1664-462X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This study was carried out to determine the factors affecting the wet grass yield of pea plants grown in Turkey. Wet grass yield was predicted using parameters such as genotype, crude protein, crude ash, acid detergent fiber (ADF), and neutral detergent fiber (NDF) with some data mining algorithms. These techniques provided easily interpretable data trees and precise cutoff values. This led to a comparison of the predictive abilities of data mining methods, including multivariate adaptive regression spline (MARS), Chi-square automatic interaction detection (CHAID), classification and regression tree (CART), and artificial neural network (ANN). To test the compatibility of the data mining algorithms, seven goodness-of-fit criteria were used. The predictive abilities of the fitted models were assessed using model fit statistics such as the coefficient of determination ( R 2 ), adjusted R 2 , root mean square error (RMSE), mean absolute percentage error (MAPE), standard deviation ratio (SD ratio), Akaike information criterion (AIC), and corrected Akaike information criterion (AICc). With the greatest R 2 and adjusted R 2 values (0.998 and 0.986) and the lowest values of RMSE, MAPE, SD ratio, AIC, and AICc (10.499, 0.7365, 0.047, 268, and 688, respectively), the MARS method was determined to be the best model for quantifying plant fresh herbage yield. In estimating the fresh herbage production of the pea plant, the results showed that the MARS method was the most appropriate model and a good substitute for other data mining techniques.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
Edited by: Mohsen Yoosefzadeh Najafabadi, University of Guelph, Canada
Reviewed by: Mehmet Korkmaz, Ordu University, Türkiye
Kismiantini Kismiantini, Yogyakarta State University, Indonesia
ISSN:1664-462X
1664-462X
DOI:10.3389/fpls.2024.1482723