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<p>Tea shoot density monitoring is crucial for quality control and yield optimization in plantations. This study developed a UAV-based multispectral imaging framework integrating machine learning for automated tea shoot assessment. We collected 3,122 ground-truth samples across four categories (tea shoots, mature leaves, dry leaves, soil) using portable spectrometry, transformed five spectral bands into 23 vegetation indices, and applied feature selection to identify optimal predictors. A two-stage classification approach was implemented: Stage-1 eliminated non-tea background with 100% accuracy; Stage-2 classified growth stages using MLP, SVM, and XGBoost algorithms. MLP achieved superior performance with 97% F1-score for tea shoots and 96% for mature leaves, outperforming SVM (87%) and XGBoost (93%). Field validation across three plantations revealed distinct temporal patterns: reverse J-shape (early budding), bell-shape (active germination), to J-shape distributions (mature canopy). Spatial uniformity was quantified using the Tea Shoot Density Index (TSDI), with the most uniform plot showing Is = − 0.876 and NNI = 1.654. This framework enables rapid plantation-wide assessment, reducing manual sampling time from days to hours while providing actionable insights for precision fertilization and irrigation management, advancing sustainable tea production.</p>

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A UAV-based multispectral imaging approach for tea shoots aerial mapping and assessment

  • 期刊论文

  • 发表日期：

    2026年1月14日

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  • Hsin-Cheng Chen,
  • Shiou-Ruei Lin,
  • Po-An Chen,
  • Ta-Te Lin

摘要

Tea shoot density monitoring is crucial for quality control and yield optimization in plantations. This study developed a UAV-based multispectral imaging framework integrating machine learning for automated tea shoot assessment. We collected 3,122 ground-truth samples across four categories (tea shoots, mature leaves, dry leaves, soil) using portable spectrometry, transformed five spectral bands into 23 vegetation indices, and applied feature selection to identify optimal predictors. A two-stage classification approach was implemented: Stage-1 eliminated non-tea background with 100% accuracy; Stage-2 classified growth stages using MLP, SVM, and XGBoost algorithms. MLP achieved superior performance with 97% F1-score for tea shoots and 96% for mature leaves, outperforming SVM (87%) and XGBoost (93%). Field validation across three plantations revealed distinct temporal patterns: reverse J-shape (early budding), bell-shape (active germination), to J-shape distributions (mature canopy). Spatial uniformity was quantified using the Tea Shoot Density Index (TSDI), with the most uniform plot showing Is = − 0.876 and NNI = 1.654. This framework enables rapid plantation-wide assessment, reducing manual sampling time from days to hours while providing actionable insights for precision fertilization and irrigation management, advancing sustainable tea production.