Akademik Veri Yönetim Sistemi
LEGUME RESEARCH, cilt.48, sa.10, ss.1635-1647, 2025 (SCI-Expanded, Scopus)
Background: In this study, a machine learning based approach was developed to classify alfalfa genetic material (one cultivar and
three synthetic genotypes) and different salt ion types [sodium chloride (NaCl), calcium chloride (CaCl2) and potassium chloride (KCl)
according to the physiological responses of these plants under three controlled temperatures.
Methods: The raw dataset included germination features; germination energy (GE), germination percentage (GP), germination index
(GI), mean germination days (MGD), root length (RL), plumule length (PL), fresh weight (FW), dry weight (DW) and seedling vigor
(SV). Model performance was assessed using ten-fold cross-validation. Classification models were trained on the dataset using
Multilayer Perception (MLP), K-nearest Neighbors (KNN), Decision Tree, Random Forest, Gradient Boosted Trees (GBT), Extreme
Gradient Boosting (XGBoost) and Tree Ensemble algorithms. The performance of the models was evaluated on the test subset using
statistical measures, such as accuracy, error rate and Cohen’s kappa coefficient.
Result: Tree Ensemble algorithm had the highest accuracy rates of 99.60% and 92.20% for the classification of alfalfa genetic
materials and salt ion types, respectively. Random forest and XGBoost, followed by accuracy rates of 99.50% and 91.20% for the
classification of alfalfa genetic materials and salt ion types, respectively. All Cohen’s kappa values were above 85.00%, indicating
that distinction between classes was successfully achieved at a high level of reliability. These findings indicate that the alfalfa
genetic material can be classified more accurately than salt ion types. DW and PL emerged as the most important features for the
classification of alfalfa genetic materials and salt ion types, respectively.