OPIA


37930849	OPIA: an open archive of plant images and related phenotypic traits. [PMID: 37930849] Cao Y, Tian D, Tang Z, Liu X, Hu W, Zhang Z, Song S. 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. Nucleic Acids Res. 2024:52(D1) \| 4 Citations (from Europe PMC, 2025-03-15)

OPIA: an open archive of plant images and related phenotypic traits. [PMID: 37930849]

Cao Y, Tian D, Tang Z, Liu X, Hu W, Zhang Z, Song S.

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.

Nucleic Acids Res. 2024:52(D1) | 4 Citations (from Europe PMC, 2025-03-15)

URL:	https://ngdc.cncb.ac.cn/opia/
Full name:	open archive of plant images and related phenotypic traits
Description:	The Open Plant Image Archive (OPIA) is a comprehensive and versatile database specifically created to catalog benchmark datasets and document the phenotypic traits of plants using high-quality images. OPIA encompasses a wide array of plant image datasets, including those of staple crops and other plant species. These datasets cover diverse growth and development stages, encompassing multi-tissue types and a broad spectrum of benchmark datasets designed for plant phenotyping applications.
Year founded:	2023
Last update:	2023.06.20
Version:	v1.0
Accessibility:	Accessible
Country/Region:	China

Data type:	Other
Data object:	Plant
Database category:	Genotype phenotype and variation
Major species:	Arabidopsis thaliana Fagopyrum esculentum Manihot esculenta Phaseolus vulgaris alphaendornavirus 1 Zea mays Brassica napus Oryza sativa Glycine max Beta vulgaris Helianthus annuus Triticum aestivum
Keywords:	image image classification plant database plant phenomics image-based traits

University/Institution:	Beijing Institute of Genomics, Chinese Academy of Sciences
Address:	No.1 Beichen West Road, Chaoyang District, Beijing 100101, China
City:	Beijing
Province/State:	Beijing
Country/Region:	China
Contact name (PI/Team):	Song Shuhui
Contact email (PI/Helpdesk):	songshh@big.ac.cn

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Database Commons a catalog of worldwide biological databases