山东农业科学2025，Vol.57Issue(10)：158-164,172,8.DOI:10.14083/j.issn.1001-4942.2025.10.020
基于RGB图像与多光谱图像数据融合的玉米表型检测
Phenotypic Detection of Maize Based on Fusion of RGB Image and Multispectral Image Data
摘要
Abstract
In order to address the issues of waste time and labor during leaf phenotype collection and inad-equate generalization performance of single-source image detection,this study proposed a deep learning-based algorithm for detecting maize leaf phenotypes(fresh weight,chlorophyll content,leaf area and leaf width)by fusing RGB images and multispectral images.The RGB images and multispectral images of maize leaves were collected at 5 to 7 leaf stage,and semantic segmentation and GrabCut algorithm were used to segment them,re-spectively.A multi-channel input method was employed to input the two types of images into the MobileNetV2 network for training and testing.The results indicated that the effect of maize leaf phenotype detection based on the fusion of RGB and multispectral images outperformed that using single-source data.The average absolute er-rors were 0.161 9,0.110 1,0.166 3 and 0.144 2 respectively for the four phenotypic traits of maize leaves,and the time consumption for each sample was less than 10 ms,meeting expectations.It showed that the proposed maize leaf phenotype detection algorithm based on multisource data fusion exhibited excellent performance and had significant application prospects in early growth monitoring and yield estimation of maize.关键词
RGB图像/多光谱图像/多源数据融合/玉米叶片表型/图像处理/多通道输入Key words
RGB images/Multispectral images/Multi-source data fusion/Maize leaf phenotype/Image processing/Multi-channel input分类
农业科学引用本文复制引用
杨琳琳,刘瑞,别书凡,李文峰,施杰..基于RGB图像与多光谱图像数据融合的玉米表型检测[J].山东农业科学,2025,57(10):158-164,172,8.基金项目
国家自然科学基金项目(32160420) （32160420）
云南省重大科技专项(202202AE09002103) （202202AE09002103）
云南省农林联合专项(202301BD070001-172) （202301BD070001-172）
