Integrating Machine Learning and In Vitro Screening to Evaluate Drought and Temperature Stress Responses for Vicia Species

OKUMUŞ O., ŞİMŞEK Ö., Isak M. A., KOÇAK N., Aydin A., Eren B., ...Daha Fazla

Processes, cilt.13, sa.6, 2025 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 6
  • Basım Tarihi: 2025
  • Doi Numarası: 10.3390/pr13061845
  • Dergi Adı: Processes
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Anahtar Kelimeler: drought stress, germination and seedling vigor, in vitro screening, machine learning, Vicia species
  • Ankara Üniversitesi Adresli: Evet

Özet

Drought and temperature extremes are major abiotic stressors limiting legume productivity worldwide. This study investigates the germination and early seedling responses of six cultivars belonging to three Vicia species (V. sativa, V. pannonica, and V. narbonensis) under varying levels of polyethylene glycol (PEG)-induced drought and temperature conditions (12 °C, 18 °C, and 24 °C) in vitro. Significant cultivar-dependent differences were observed in the germination rate (GR), shoot and root length (SL and RL), fresh and dry weight (FW and DW), and vigor index (VI). The Ayaz cultivar exhibited superior performance, particularly under severe drought (10% PEG) and optimal temperature (24 °C), while Özgen and Balkan were most sensitive to stress. Principal component and correlation analyses revealed strong associations between the vigor index, shoot height, and fresh and dry weight, particularly in high-performing genotypes. To further model and predict stress responses, four machine learning (ML) algorithms—Random Forest (RF), k-Nearest Neighbors (k-NNs), Multilayer Perceptron (MLP), and Support Vector Machines (SVMs)—were employed. Based on model performance metrics, and considering high R2 values along with low RMSE and MAE values, the MLP model demonstrated the most accurate predictions for the GR (R2 = 0.95, RMSE = 0.06, MAE = 0.05) and VI (R2 = 0.99, RMSE = 0.02, MAE = 0.01) parameters. In contrast, the RF model yielded the best results for the SL (R2 = 0.98, RMSE = 0.02, MAE = 0.02) and DW (R2 = 0.93, RMSE = 0.06, MAE = 0.04) parameters, while the highest prediction accuracy for the RL (R2 = 0.83, RMSE = 0.09, MAE = 0.07) and FW (R2 = 0.97, RMSE = 0.05, MAE = 0.03) parameters was achieved using the SVM model. Comparative analysis with recent studies confirmed the applicability of ML in stress physiology and genotype screening. This integrative approach offers a robust framework for genotype selection and stress tolerance modeling in legumes, contributing to developing climate-resilient crops.