农业机械学报2025，Vol.56Issue(1)：84-91,101,9.DOI:10.6041/j.issn.1000-1298.2025.01.009
基于RGB与深度图像融合的生菜表型特征估算方法
Lettuce Phenotype Estimation Using Integrated RGB-Depth Image Synergy
摘要
Abstract
Accurate measurement of phenotypic traits in plant growth using automated methods is crucial for applications such as breeding and cultivation.Aiming to address the need for non-destructive,precise detection of phenotypic traits in factory-grown lettuce,by integrating RGB images and depth images collected by depth cameras,an improved DeepLabv3+model was used for image segmentation,and a dual-modal regression network estimated the phenotypic traits of lettuce.The backbone of the improved segmentation model was replaced from Xception to MobileViTv2 to enhance its global perception capabilities and performance.In the regression network,a convolutional multi-modal feature fusion module(CMMCM)was proposed to estimate the phenotypic traits of lettuce.Experimental results on a public dataset containing four lettuce varieties showed that the method estimated five phenotypic traits—fresh weight,dry weight,canopy diameter,leaf area,and plant height—with determination coefficients of 0.922 2,0.931 4,0.862 0,0.935 9,and 0.887 5,respectively.Compared with the RGB and depth image-based phenotypic parameter estimation benchmark ResNet-10(Dual)without CMMCM and SE modules,the improved model increased the determination coefficients by 2.54％,2.54％,1.48％,2.99％,and 4.88％,respectively,with an image detection time of 44.8 ms per image.This demonstrated that the method achieved high accuracy and real-time performance for non-destructive detection of lettuce phenotypic traits through dual-modal image fusion.关键词
生菜/表型估算/模态融合/分割模型/RGB图像/深度图像Key words
lettuce/phenotypic estimation/modality fusion/segmentation model/RGB images/depth images分类
计算机与自动化引用本文复制引用
陆声链,李沂杨,李帼,贾小泽,鞠青青,钱婷婷..基于RGB与深度图像融合的生菜表型特征估算方法[J].农业机械学报,2025,56(1):84-91,101,9.基金项目
国家自然科学基金项目(61762013)、上海市农业科技创新项目(2023-02-08-00-12-F04621)和农业农村部长三角智慧农业技术重点实验室开放课题(KSAT-YRD2023011) （61762013）
