Journal of Research and Education(JRE)

Milas Directorate of Forestry, Turkey

Publications

• Research Article   
  Predicting Olive Yield in Mediterranean Climate Zones of Turkiye Using Remote Sensing and Artificial Neural Networks: A Case Study of Mugla Province
  Author(s): Cagatay Nalcaoglu*, Durdane Hilal Nalcaoglu, Aynur Dogru and Sezginyaprak
  
  This study focuses on predicting olive yield in the Mugla province of Turkey—one of the country’s major olive production regions— using remote sensing data and artificial neural networks (ANN), a machine learning approach. The research integrates multi- source data, including Sentinel-2 and MODIS satellite imagery (NDVI, LST, GPP), meteorological data from the Turkish State Meteorological Service, and soil parameters from the Soil Grids database. These multidimensional datasets were used to train and evaluate an ANN-based model to predict annual olive yield at the district level between 2020 and 2024. The ANN model demonstrated high predictive performance, with a test R2 of 0.82, RMSE of 0.18 t/ha, and MAE of 0.12 t/ha, outperforming alternative models such as XGBoost. The results confirmed strong positive correlations between NDVI and GPP with yield, and a negative .. Read More»
  

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