河南农业大学学报2026，Vol.60Issue(2)：337-346,10.DOI:10.16445/j.cnki.1000-2340.20260227.001
"作物-秸秆-土壤"图像分割与比例提取方法研究
Research on"crop-straw-soil"segmentation and proportional extraction method
摘要
Abstract
[Objective]This paper presents an in-depth research on the"crop-straw-soil"segmentation and proportion extraction methods to improve the automation level of field straw coverage surveys.[Method]High-definition digital images of crop,straw,and soil were collected in field conditions.U-Net and DeepLabV3+models with backbone networks including VGG16,ResNet50,and Efficient-Net_B0,were designed for"crop-staw-soil"segmentation.Based on the segmentation results,the model with the best performance was selected for proportion extraction.Segmentation accuracy was evaluated using mean intersection over union(MI),Ac,and Re.The accuracy of the proportion extrac-tion was assessed by the coefficient of determination(R2)and root mean square error(RM).[Result]The ResNet50-based U-Net achieved precise segmentation of farmland images with an MI of 80.96%and an accuracy of 90.00%,significantly outperforming the ResNet50-based DeepLabV3+model and other main trunk feature netwok,with an MI of 80.78%and an Ac of 89.92%.The ResNet50-based U-Net achieved the highest accuracy coverage extraction,with an R2 of 0.851-0.979 and an RM of 3.220%-8.554%.[Conclusion]The ResNet50-based U-Net model can accurately extract"crop-straw-soil"coverage information from farmland digital images,providing technical support for dynamically monitoring farming progress and promoting agricultural ecological environmental protection.关键词
秸秆/土壤/作物/图像分割/比例提取Key words
straw/soil/crop/image segmentation/proportional extraction分类
农业科技引用本文复制引用
葛士里,高光甫,岳继博,刘杨,冯海宽,李冰,乔红波,郭伟,束美艳.."作物-秸秆-土壤"图像分割与比例提取方法研究[J].河南农业大学学报,2026,60(2):337-346,10.基金项目
国家自然科学基金项目(42101362) （42101362）
河南省科技攻关计划项目(232102321103) （232102321103）
河南省高等学校重点科研项目(25A520027) （25A520027）
