ArticleNucleic acids research2024
OPIA: an open archive of plant images and related phenotypic traits.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Img2Variety: Image-based intraspecific varieties identification across the whole growth period.Plant phenomics (Washington, D.C.) · 2026Article
- Database resources of the National Genomics Data Center, China National Center for Bioinformation in 2026.Nucleic acids research · 2026Article
- Variation of fruit phenotypic traits of Cinnamomum camphora from 11 provenances in China.PloS one · 2026Article
- Deep-learning-ready RGB-depth images of seedling development.Plant methods · 2025Article
- Plant genomic resources at National Genomics Data Center: assisting in data-driven breeding applications.aBIOTECH · 2024Review
- Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024.Nucleic acids research · 2024Article
- The 2024 Nucleic Acids Research database issue and the online molecular biology database collection.Nucleic acids research · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
High-throughput plant phenotype acquisition technologies have been extensively utilized in plant phenomics studies, leading to vast quantities of images and image-based phenotypic traits (i-traits) that are critically essential for accelerating germplasm screening, plant diseases identification and biotic & abiotic stress classification. Here, we present the Open Plant Image Archive (OPIA, https://ngdc.cncb.ac.cn/opia/), an open archive of plant images and i-traits derived from high-throughput phenotyping platforms. Currently, OPIA houses 56 datasets across 11 plants, comprising a total of 566 225 images with 2 417 186 labeled instances. Notably, it incorporates 56 i-traits of 93 rice and 105 wheat cultivars based on 18 644 individual RGB images, and these i-traits are further annotated based on the Plant Phenotype and Trait Ontology (PPTO) and cross-linked with GWAS Atlas. Additionally, each dataset in OPIA is assigned an evaluation score that takes account of image data volume, image resolution, and the number of labeled instances. More importantly, OPIA is equipped with useful tools for online image pre-processing and intelligent prediction. Collectively, OPIA provides open access to valuable datasets, pre-trained models, and phenotypic traits across diverse plants and thus bears great potential to play a crucial role in facilitating artificial intelligence-assisted breeding research.
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Registered trials
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.