A Novel Ground-Based Perception System for Estimating Vegetation Above-Ground Biomass in Typical Grasslands

Abstract

Accurate estimation of Above-Ground Biomass (AGB) in natural grasslands is critical for ecosystem management, yet traditional satellite and uncrewed aerial vehicle (UAV) remote sensing struggles to capture fine-scale vegetation details. To overcome these limitations, we developed “PraePercep-Y,” an integrated ground-based robotic perception platform tailored for typical grasslands. Within this system, we systematically established a theoretical model for fractional vegetation cover (FVC) estimation, determining that an optimal actual pixel area of 3–5 mm2 significantly enhances detection accuracy. Building upon this optimized data acquisition, we proposed the AGBT network for high-precision biomass inversion. The AGBT model is designed with a flexible dual-stream architecture that operates in two primary configurations: Mode 1 (RGB-based estimation), utilizing the visual stream to extract hierarchical RGB image features, and Mode 2 (Multisensor data fusion), which further integrates an attribute stream to encode vegetation height and FVC. To effectively fuse these modalities, we introduced a cross-modal attention mechanism for semantic alignment and an adaptive weight distribution strategy to dynamically adjust modality contributions. Validated through rigorous field experiments from 2022 to 2025 across 600 quadrats in Inner Mongolia, the system demonstrated superior performance. The AGBT model achieved strong predictive accuracy, with R2 values of 0.82 for Mode 1 (RGB-only) and 0.88 for Mode 2 (multisensor fusion). This study provides a highly accurate, automated methodological baseline for AGB assessment, significantly reducing manual labor and offering a scientific basis for grassland resource conservation.