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

Abstract

Abstract (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.

Plant phenotyping relevance

UAVマルチスペクトル画像から大麦の成熟・生育状態を推定し、複数の機械学習手法を比較する予測フレームワークが研究の中心であるため、植物状態の実質的なフェノタイピング手法に該当する。

Code and data availability

The paper's UAV multispectral imagery, grain quality measurements, and ML datasets are not publicly available; the Data Availability statement restricts them to requests to the corresponding author. No author code or model deposit is mentioned.

No evidence-backed public reproduction asset is currently recorded.