Multimodal Data Fusion for Precise Lettuce Phenotype Estimation Using Deep Learning Algorithms
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Non UBC
department or school: Biology, Department of (Okanagan)
data provider: DSpace
identifier citation: Plants 13 (22): 3217 (2024)
creator: Hou, Lixin
creator: Zhu, Yuxia
creator: Wang, Mengke
creator: Wei, Ning
creator: Dong, Jiachi
creator: Tao, Yaodong
creator: Zhou, Jing
creator: Zhang, Jian (Professor of biology)
issued: 2024-11-29T22:00:11Z
issued: 2024-11-15
description: Effective lettuce cultivation requires precise monitoring of growth characteristics, quality assessment, and optimal harvest timing. In a recent study, a deep learning model based on multimodal data fusion was developed to estimate lettuce phenotypic traits accurately. A dual-modal network combining RGB and depth images was designed using an open lettuce dataset. The network incorporated both a feature correction module and a feature fusion module, significantly enhancing the performance in object detection, segmentation, and trait estimation. The model demonstrated high accuracy in estimating key traits, including fresh weight (fw), dry weight (dw), plant height (h), canopy diameter (d), and leaf area (la), achieving an R² of 0.9732 for fresh weight. Robustness and accuracy were further validated through 5-fold cross-validation, offering a promising approach for future crop phenotyping.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/89761
note: Citation: Hou, L.; Zhu, Y.; Wang, M.;Wei, N.; Dong, J.; Tao, Y.; Zhou, J.;Zhang, J. Multimodal Data Fusion forPrecise Lettuce Phenotype EstimationUsing Deep Learning Algorithms.Plants 2024, 13, 3217. https://doi.org/10.3390/plants13223217Academic Editors: Guoxiong Zhou,Liujun Li and Xiaoyulong ChenReceived: 22 October 2024Revised: 5 November 2024Accepted: 14 November 2024Published: 15 November 2024Copyright: © 2024 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).ArticleMultimodal Data Fusion for Precise Lettuce PhenotypeEstimation Using Deep Learning AlgorithmsLixin Hou 1 , Yuxia Zhu 1, Mengke Wang 1, Ning Wei 1, Jiachi Dong 1, Yaodong Tao 2, Jing Zhou 1,*and Jian Zhang 3,4,*1 College of Information and Technology, Jilin Agricultural University, Changchun 130118, China;lixinh@jlau.edu.cn (L.H.); 20221152@mails.jlau.edu.cn (Y.Z.); 20241615@mails.jlau.edu.cn (M.W.);20231296@mails.jlau.edu.cn (N.W.); 2212030329@mails.jlau.edu.cn (J.D.)2 School of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, China;ydtao@bjtu.edu.cn3 Faculty of Agronomy, Jilin Agricultural University, Changchun 130118, China4 Department of Biology, University of British Columbia, Okanagan, Kelowna, BC V5K1K5, Canada* Correspondence: 012028@jlau.edu.cn (J.Z.); jian.zhang@ubc.ca (J.Z.)Abstract: Effective lettuce cultivation requires precise monitoring of growth characteristics, qualityassessment, and optimal harvest timing. In a recent study, a deep learning model based on multi-modal data fusion was developed to estimate lettuce phenotypic traits accurately. A dual-modalnetwork combining RGB and depth images was designed using an open lettuce dataset. The networkincorporated both a feature correction module and a feature fusion module, significantly enhancingthe performance in object detection, se…
has type: Article
is shown at: 10.14288/1.0447378
language: eng
peer review status: Reviewed
provider: Vancouver : University of British Columbia Library
publisher: Multidisciplinary Digital Publishing Institute
publisher DOI: 10.3390/plants13223217
rights: CC BY 4.0
rights URI: https://creativecommons.org/licenses/by/4.0/
scholar level: Faculty
scholar level: Researcher
subject: deep learning
subject: phenotype
subject: lettuce
subject: RGB-D
type: Text
identifier URI: http://hdl.handle.net/2429/89761