结合光谱与形态学植物特征及决策树模型，利用多年无人机数据估算早期棉花生物量与氮素状况

中文摘要 AI 辅助

棉花的精准氮素管理（PNM）需要在生长季内监测作物生长参数和氮素状况指标，以确定施肥时机、位置和施用量，从而实现冠层发育和产量的最优化。本研究开发了基于遥感和机器学习的方法，用于估算棉花干生物量重量（DBW）、植株氮素吸收量（PNU）、植株氮素浓度（PNC）、临界氮稀释曲线（Nc）和氮素营养指数（NNI），为精准氮素管理提供支持。为此，开展了为期三年的田间氮素管理研究，并在营养生长早期至开花阶段（对施肥至关重要）获取了基于无人机（UAV）的多光谱图像。时空一致的光谱和形态学植物特征，包括株高（PH）和冠层覆盖度分数（FCC），为模型训练提供了可靠的输入。采用试验留一法（THO）和留一年法（LOYO）验证方法，对三类模型的估算效果进行了评估：一是仅使用植被指数（VIs）的简单回归，二是结合VIs、PH和FCC的多元线性回归（MLR），三是结合光谱反射率、PH和FCC的决策树模型，包括随机森林回归（RFR）和极端梯度提升（XGB）。最佳验证精度来自RFRTHO（DBW的R²为0.88、平均绝对百分比误差（MAPE）为23.14%；PNU的R²为0.84、MAPE为20.61%；PNC的R²为0.85、MAPE为7.82%）和XGBTHO（DBW的R²为0.87、MAPE为21.91%；PNU的R²为0.81、MAPE为21.40%；PNC的R²为0.86、MAPE为7.66%）。Nc由模型估算的DBW和PNC计算得出，适用于在得克萨斯州沿海平原种植的中高株型高产品种棉花，并通过地面实测生物量进行验证。从XGBTHO输出推导得到的NNI，在识别氮素亏缺地块和多级氮素胁迫分类方面，表现略优于从RFRTHO输出推导得到的NNI。

英文摘要

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.

发表机构

• Texas A&M University（得克萨斯农工大学）
• Utah State University（犹他州立大学）
• Mississippi State University（密西西比州立大学）
• University of California, Davis（加利福尼亚大学戴维斯分校）
• Kansas State University（堪萨斯州立大学）

机构由 AI 辅助整理，请以论文原文为准。