{"title":"BreedingEIS: An Efficient Evaluation Information System for Crop Breeding.","authors":"Kaijie Qi, Xiao Wu, Chao Gu, Zhihua Xie, Shutian Tao, Shaoling Zhang","doi":"10.34133/plantphenomics.0029","DOIUrl":null,"url":null,"abstract":"<p><p>Crop breeding programs generate large datasets. Thus, it is difficult to ensure the accuracy and integrity of all the collected data in the breeding process. To improve breeding efficiency, we established an open source and free breeding evaluation information system (BreedingEIS). The full system is composed of a web client and a mobile client. The web client is used to name the individual breeding offspring plants and analyze data. The mobile client is based on the technology of widely used smartphones and is suitable for Android and iOS systems. Its functions focus on field evaluation, including quick response code recognition, evaluation data entry, and real-time viewing. In addition, near-field communication technology and portable label machines are introduced to enable breeders to quickly locate each individual plant and accurately label any samples collected from it. Generally, BreedingEIS enables users to accurately and conveniently register phenotypic data and quickly lock target individual plants from large volumes of data. The system provides a low-cost and highly efficient solution for crop information evaluation and enables breeders to better collect, manage, and use breeding data for decision making, which is a valuable resource for crop breeding.</p>","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":"5 ","pages":"0029"},"PeriodicalIF":7.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10014329/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Plant Phenomics","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.34133/plantphenomics.0029","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0
Abstract
Crop breeding programs generate large datasets. Thus, it is difficult to ensure the accuracy and integrity of all the collected data in the breeding process. To improve breeding efficiency, we established an open source and free breeding evaluation information system (BreedingEIS). The full system is composed of a web client and a mobile client. The web client is used to name the individual breeding offspring plants and analyze data. The mobile client is based on the technology of widely used smartphones and is suitable for Android and iOS systems. Its functions focus on field evaluation, including quick response code recognition, evaluation data entry, and real-time viewing. In addition, near-field communication technology and portable label machines are introduced to enable breeders to quickly locate each individual plant and accurately label any samples collected from it. Generally, BreedingEIS enables users to accurately and conveniently register phenotypic data and quickly lock target individual plants from large volumes of data. The system provides a low-cost and highly efficient solution for crop information evaluation and enables breeders to better collect, manage, and use breeding data for decision making, which is a valuable resource for crop breeding.
期刊介绍:
Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals.
The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.
The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.