Integrating spectral and morphological plant features with decision-tree models for early-season cotton biomass and nitrogen status estimation from multi-year UAV data

2026-08-07

AI Summary

This study addresses the need for precise estimation of biomass and nitrogen status during early cotton growth to support variable-rate fertilization. By integrating spectral indices derived from UAV-based multispectral imagery with morphological traits—such as plant height and canopy cover—and employing machine learning models including Random Forest Regression (RFR) and Extreme Gradient Boosting (XGB), the research predicts dry matter weight, plant nitrogen uptake, and nitrogen concentration. Innovatively leveraging multi-year field trials with spatiotemporally consistent spectral and morphological features, the study evaluates model generalizability through leave-one-year-out cross-validation. Results demonstrate that RFR and XGB achieve superior performance, with mean absolute percentage error (MAPE) for nitrogen concentration estimation below 8%. The derived Nitrogen Nutrition Index effectively discriminates among multiple levels of nitrogen stress.

Abstract

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.

Problem

• cotton biomass
• nitrogen status
• precision nitrogen management
• UAV remote sensing
• early-season estimation

Innovation

• UAV remote sensing
• decision-tree models
• nitrogen nutrition index
• morphological features
• precision nitrogen management