Classification and Prediction of Growth Conditions in Food Barley Fields Using UAV Multispectral Images and Machine Learning Approaches

(150 to 250 words) Spatial variability in barley maturation complicates preharvest classification for food-grade production and accurate conventional field-based assessments. Therefore, we classified barley growth into food-grade–oriented categories and developed a predictive framework integrating UAV-based multispectral imagery with machine learning. Grain quality metrics were first analyzed using principal component analysis and k-means clustering to define three physiologically distinct growth levels. Multi-temporal vegetation indices (NDVI, GNDVI, and NDRE) were extracted from UAV imagery, and key predictors were selected using Lasso regularization. Comparisons of Random Forest (RF), XGBoost, and Support Vector Machine models indicated that tree-based ensembles achieved high classification accuracy (>0.9), with late-season NDVI identified as the most influential predictor. Spatial mapping using the RF model revealed pronounced inter-field variability, identifying zones of incomplete maturation associated with lodging. Overall, integrating grain quality traits with UAV-based spectral monitoring allows accurate field-scale classification of food-grade barley growth and informs site-specific harvest management.