Research on an automated mapping method for rice aboveground biomass based on low-altitude remote sensing

Abstract

Accurate monitoring of rice aboveground biomass (AGB) is crucial for guiding agricultural production management. This study focuses on high-precision estimation and automated mapping of rice AGB. Field experiments were conducted in Nanxun District, Huzhou City, Zhejiang Province. We collected UAV RGB and multispectral images, rice AGB, and plant height data. By integrating vegetation indices, texture features, and plant height information, the AGB estimation model was established using algorithms such as Stacking. A framework combining "SAM + MobileNetV3-Small classification" was proposed to achieve automated paddy field extraction and phenology recognition. The results demonstrate that the rice AGB prediction model based on the Stacking ensemble algorithm performed excellently. The introduction of plant height significantly improved model accuracy. For example, during the heading stage, R2 increased from 0.421 to 0.739, and RPIQ rose from 1.995 to 3.015. The automated paddy field extraction and phenology recognition framework developed in this study achieved a segmentation accuracy of 0.968 and a classification accuracy of 0.993 on the dataset used in this study, without requiring manual annotation. This research provides a technical reference for automated and high-precision mapping of rice AGB.