Phenotype Analysis of Arabidopsis thaliana Based on Optimized Multi-Task Learning

Peisen Yuan (), Shuning Xu, Zhaoyu Zhai () and Huanliang Xu
Additional contact information
Peisen Yuan: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Shuning Xu: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Zhaoyu Zhai: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Huanliang Xu: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China

Mathematics, 2023, vol. 11, issue 18, 1-18

Abstract: Deep learning techniques play an important role in plant phenotype research, due to their powerful data processing and modeling capabilities. Multi-task learning has been researched for plant phenotype analysis, which can combine different plant traits and allow for a consideration of correlations between multiple phenotypic features for more comprehensive analysis. In this paper, an intelligent and optimized multi-task learning method for the phenotypic analysis of Arabidopsis thaliana is proposed and studied. Based on the VGG16 network, hard parameter sharing and task-dependent uncertainty are used to weight the loss function of each task, allowing parameters associated with genotype classification, leaf number counting, and leaf area prediction tasks to be learned jointly. The experiments were conducted on the Arabidopsis thaliana dataset, and the proposed model achieved weighted classification accuracy, precision, and F w scores of 96.88 % , 97.50 % , and 96.74 % , respectively. Furthermore, the coefficient of determination R 2 values in the leaf number and leaf area regression tasks reached 0.7944 and 0.9787, respectively.

Keywords: plant phenotype; multi-task learning; VGG16; hard parameter sharing; Arabidopsis thaliana (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/18/3821/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/18/3821/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:18:p:3821-:d:1233914

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().