用无人机数据和决策树模型，早期精准估算棉花生物量与氮状况。
Integrating spectral and morphological plant features with decision-tree models for early-season cotton biomass and nitrogen status estimation from multi-year UAV data
- 融合光谱与植株形态特征，用随机森林和梯度提升树建模。
- 预测干重、氮吸收量和浓度的决定系数达0.88、0.84、0.85。
- 适合棉田精准施肥管理，尤其适用于大田多季监测场景。
棉花精准氮肥管理（PNM）需要在生长季内监测作物生长参数与氮素状态指标，以确定肥料施用时机、位置和用量，实现冠层优化与高产。本研究基于三年田间氮管理试验，利用无人机获取早生长期至开花期的多光谱影像，提取时空一致的光谱与植株形态特征（如株高PH、冠层覆盖度FCC），构建了干物质重量（DBW）、植株氮吸收量（PNU）、植株氮浓度（PNC）、临界氮稀释线（Nc）及氮营养指数（NNI）的估测模型。通过简单回归、多元线性回归（MLR）以及结合光谱、株高和冠层覆盖度的随机森林（RFR）与极端梯度提升（XGB）模型进行评估，采用留一季外（LOYO）与留样外（THO）验证方法。最优结果为RFR_THO（DBW：R²=0.88，MAPE=23.14%；PNU：R²=0.84，MAPE=20.61%；PNC：R²=0.85，MAPE=7.82%）和XGB_THO（DBW：R²=0.87，MAPE=21.91%；PNU：R²=0.81，MAPE=21.40%；PNC：R²=0.86，MAPE=7.66%）。Nc由模型估算的DBW与PNC计算得出，针对德克萨斯州海岸平原高产中高秆棉花品种，并通过地面实测生物量验证。基于XGB_THO输出的NNI在识别缺氮地块和多级氮胁迫分类上表现略优于RFR_THO。
原文摘要 · Abstract (English)
Precision nitrogen (N) management (PNM) for cotton requires in-season monitoring of crop growth parameters and N status indicators to decide fertilizer timing, placement, and application rates for optimal canopy development and yield. This study developed remote sensing and machine learning-based methods to estimate cotton dry biomass weight (DBW), plant N uptake (PNU), plant N concentration (PNC), critical N dilution (Nc), and nitrogen nutrition index (NNI) to support PNM. To achieve this, a three-year field-based N-management study was conducted and unmanned aerial vehicle (UAV)-based multispectral images were acquired between early vegetative growth and flowering stages, critical for fertilizer applications. Spatiotemporally consistent spectral and morphological plant features, including plant height (PH) and fractional canopy cover (FCC), provided reliable model training inputs. DBW, PNU, and PNC estimates from simple regression using vegetation indices (VIs), multiple linear regression (MLR) combining VIs, PH, and FCC, and decision-tree models, random forest regression (RFR) and extreme gradient boosting (XGB), combining spectral reflectance, PH, and FCC were evaluated using trial-held-out (THO) and leave-one-year-out (LOYO) validation methods. The best validation accuracies were from RFRTHO (R2 = 0.88 and MAPE = 23.14% for DBW; R2 = 0.84 and MAPE = 20.61% for PNU; R2 = 0.85 and MAPE = 7.82% for PNC) and XGBTHO (R2 = 0.87 and MAPE = 21.91% for DBW; R2 = 0.81 and MAPE = 21.40% for PNU; R2 = 0.86 and MAPE = 7.66% for PNC). Nc was calculated from model estimated DBW and PNC for high-yielding, medium-to-tall cotton varieties grown in the Texas Coastal Plains and validated using ground-truth biomass measurements. NNI derived from XGBTHO outputs performed marginally better than NNI from RFRTHO in identifying N-deficient plots and multi-level N-stress categorization.
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