Metin Zontul

Predicting Lentil Seed Yield from Agronomic Traits Using a Foundation-Augmented Machine Learning Ensemble

Accurate agricultural yield prediction is crucial for crop management and data-driven decision-making. This study proposes a machine learning framework for predicting total lentil seed yield using 264 observations collected during a single growing season at one location in Sivas,...

Explainable Machine Learning for Methylene Blue Removal Under Irradiation: Assessing Nominal Cu-Loading in AC@NiO

The time-dependent removal performance under irradiation of activated-carbon-supported nickel oxide (AC@NiO) samples containing nominal Cu-loadings of 0%, 3%, 5%, 7.5%, and 10% was examined using experimental concentration data and an explainable machine-learning (ML) framework....