What question did this study set out to answer?

The aim is to enhance biomass estimation of rye cover crops using UAV imagery and machine learning methods.

February 25, 2026Open Access

Using Multispectral UAV Imagery for Rye Biomass Estimation and SEM-Based Attribution Analysis

WLWenyi LuChina University of Geosciences XZXi ZhangGeneral Cardiology MKMasakazu KomatsuzakiIbaraki University

Key Points

The aim is to enhance biomass estimation of rye cover crops using UAV imagery and machine learning methods.
Utilized low-altitude UAV for high-resolution imagery
Measured height, SPAD values, and dry biomass of rye
Applied machine learning for crop canopy height, VIs, and VF estimation
Employed structural equation modeling to analyze parameter contributions
Compared single and multi-parameter biomass estimation models
Single-parameter height estimation model achieved R2 = 0.935 overall
Vegetation indices performed best at the non-seedling stage (R2 = 0.851)
Multi-parameter models combining height, VF, and VIs improved estimation, achieving R2 = 0.951
Structural equation modeling showed synergistic effects of parameters on biomass accumulation

Abstract

Effective management of rye cover crops in cash-crop systems relies heavily on accurate biomass estimation. Low-altitude Unmanned Aerial Vehicle (UAV) imagery offers a promising high-resolution alternative, yet unlocking its full potential requires moving beyond basic estimation models to more integrative and explanatory models. This study obtains the measured height (MH), SPAD (Soil and Plant Analyzer Development) values, and measured dry biomass (MDB) and applies UAV remote sensing and machine learning to acquire the crop canopy height, vegetation indices (VIs), and vegetation fraction (VF) across growth stages. Among single-parameter biomass estimation models, the estimated height yields the best at the overall growth stage (R2 = 0.935), whereas selected VIs perform the best at the non-seedling stage (R2 = 0.851). For multi-parameters modeling, models combining height, VF, and VIs significantly outperform the single-parameter models, achieving better estimation results throughout each growth stage (Best R2 = 0.951). Structural equation modeling clarifies the direct and indirect contributions of these parameters to biomass accumulation, revealing their synergistic effects. This study demonstrates the potential of UAV-based multi-parameter biomass estimation model to support more informed decisions in cover crop management and to advance broader precise agriculture practices. Additionally, the analytical framework developed here offers a transferable approach for high-resolution biomass monitoring in other crop systems.

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