Investigation of factors affecting fresh herbage yield in pea (Pisum arvense L.) using data mining algorithms - Çatal, Muhammed İkbal; Çelik, Şenol; Bakoğlu, Adil (Frontiers in Plant Science, 2024)
This study was carried out to determine the factors affecting the wet grass yield of pea plants grown in Turkey. It was predicted the wet grass yield by using parameters such as genotype, crude protein, crude ash, acid detergent fiber (ADF), and neutral detergent fiber (NDF) with some data mining algorithms.Certain data mining techniques were used to examine the data, yielding easily interpreted data trees and precise cutoff values for the data. 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).In order to test the compatibility of the data mining algorithms, 7 goodness of fit criteria was used. The predictive abilities of the fitted models were assessed using model fit statistics such as coefficient of determination (R 2 ), adjusted R 2 , root of 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.