Evidence map›Paper›PMID 39599426›Full record

ArticlePlants (Basel, Switzerland)2024

Multimodal Data Fusion for Precise Lettuce Phenotype Estimation Using Deep Learning Algorithms.

Lixin Hou, Yuxia Zhu, Mengke Wang, Ning Wei, Jiachi Dong, Yaodong Tao, Jing Zhou, Jian Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plants (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Lixin HouCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, China.ORCID 0000-0001-8626-9155
Yuxia ZhuCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, China.
Mengke WangCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, China.
Ning WeiCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, China.
Jiachi DongCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, China.
Yaodong TaoSchool of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, China.
Jing ZhouCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, China.ORCID 0009-0007-8884-9824
Jian ZhangFaculty of Agronomy, Jilin Agricultural University, Changchun 130118, China.ORCID 0000-0003-1507-4718

Funding

ilin Agricultural University high-level researcher grant JLAUHLRG20102006Jilin Province Science and Technology Development Plan Project No.20230101343JCJilin Provincial Department of Human Resources and Social Security employing units flexible in-troduction of talents No.201020012
6 · The paper itself

Abstract

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 R

Indexed as

deep learninglettucephenotypeRGB-D

Identifiers

PMID39599426
PMCPMC11598037

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.