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

[2608.07801] Integrating spectral and morphological plant features with decision-tree models for early-season cotton biomass and nitrogen status estimation from multi-year UAV data Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Image and Video Processing arXiv:2608.07801 (eess) [Submitted on 7 Aug 2026 ( v1 ), last revised 14 Aug 2026 (this version, v2)] Title: Integrating spectral and morphological plant features with decision-tree models for early-season cotton biomass and nitrogen status estimation from multi-year UAV data Authors: Vaishali Swaminathan , Nithya Rajan , J Alex Thomasson , Amrit Shrestha , Karem Meza Capcha , Robert Hardin , Pramod Pokhrel View a PDF of the paper titled Integrating spectral and morphological plant features with decision-tree models for early-season cotton biomass and nitrogen status estimation from multi-year UAV data, by Vaishali Swaminathan and 6 other authors View PDF 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. Subjects: Image and Video Processing (eess.IV) ; Machine Learning (cs.LG) Cite as: arXiv:2608.07801 [eess.IV] (or arXiv:2608.07801v2 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2608.07801 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Vaishali Swaminathan [ view email ] [v1] Fri, 7 Aug 2026 22:46:06 UTC (1,654 KB) [v2] Fri, 14 Aug 2026 18:07:51 UTC (1,654 KB) Full-text links: Access Paper: View a PDF of the paper titled Integrating spectral and morphological plant features with decision-tree models for early-season cotton biomass and nitrogen status estimation from multi-year UAV data, by Vaishali Swaminathan and 6 other authors View PDF view license Current browse context: eess.IV < prev | next > new | recent | 2026-08 Change to browse by: cs cs.LG eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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