农业机械学报2026，Vol.57Issue(3)：87-96,10.DOI:10.6041/j.issn.1000-1298.2026.03.009
基于UAV图像和SAM弱监督学习的黑土区保护性耕作玉米秸秆识别方法
Maize Straw Identification Method for Conservation Tillage in Black Soil Area Based on UAV Imagery and SAM Weak Supervision Learning
摘要
Abstract
Straw mulching is an important measure for conservation tillage in the black soil region.Straw identification is of great significance for the assessment of conservation tillage implementation effects and agricultural management decisions.To address the issue that fully supervised deep learning straw identification methods due to their reliance on a large amount of pixel-level labeled data,a weakly supervised learning straw identification method was proposed based on unmanned aerial vehicle(UAV)images and the segment anything model(SAM).By fine-tuned SAM with an adapter and a boundary-aware joint loss function,and generating high-quality pseudo-labels from bounding box weak annotations,an improved U-Net segmentation network was ultimately trained to achieve straw identification.A straw extraction experiment was conducted in the conservation tillage area of corn in Lishu County,Jilin Province as the research area.The results showed that the fine-tuned SAM achieved an MIoU of 81.04% and an F1-score of 87.85%,significantly outperforming the un-fine-tuned model.The model combining SAM weak supervision and the improved U-Net achieved a higher performance than other segmentation methods,with an F1-score of 90.6%.Ablation experiments verified the effectiveness of the joint loss function and convolutional modules in improving model performance.The research provided an efficient and cost-effective solution for remote sensing identification of straw identification in maize conservation tillage in the black soil region.关键词
玉米秸秆识别/无人机图像/保护性耕作/弱监督学习/SAM/语义分割Key words
maize straw recognition/unmanned aerial vehicle imagery/conservation tillage/weakly supervised learning/segment anything model/semantic segmentation分类
农业科技引用本文复制引用
赵丽华,张超,王贝贝,陈畅,武亚楠,杨翠翠,李媛媛..基于UAV图像和SAM弱监督学习的黑土区保护性耕作玉米秸秆识别方法[J].农业机械学报,2026,57(3):87-96,10.基金项目
国家重点研发计划项目(2024YFD1500805) （2024YFD1500805）
