CropQuant: An automated and scalable field phenotyping platform for crop monitoring and trait measurements to facilitate breeding and digital agriculture

Automated phenotyping technologies are capable of providing continuous and precise measurements of traits that are key to today’s crop research, breeding and agronomic practices. In additional to monitoring developmental changes, high-frequency and high-precision phenotypic analysis can enable both accurate delineation of the genotype-to-phenotype pathway and the identification of genetic variation influencing environmental adaptation and yield potential. Here, we present an automated and scalable field phenotyping platform called CropQuant, designed for easy and cost-effective deployment in different environments. To manage infield experiments and crop-climate data collection, we have also developed a web-based control system called CropMonitor to provide a unified graphical user interface (GUI) to enable realtime interactions between users and their experiments. Furthermore, we established a high-throughput trait analysis pipeline for phenotypic analyses so that lightweight machine-learning modelling can be executed on CropQuant workstations to study the dynamic interactions between genotypes (G), phenotypes (P), and environmental factors (E). We have used these technologies since 2015 and reported results generated in 2015 and 2016 field experiments, including developmental profiles of five wheat genotypes, performance-related traits analyses, and new biological insights emerged from the application of the CropQuant platform.

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Field Value
Author Zhou, Ji
Last Updated August 7, 2019, 10:54 (UTC)
Created July 31, 2019, 16:29 (UTC)
Article Host Type publisher
Article Is Open Access true
Article License Type cc-by-nc-nd
Article Version Type publishedVersion
Citation Report https://scite.ai/reports/10.1101/161547
DFW Organisation EI
DFW Work Package 4
DOI 10.1101/161547
Date Last Updated 2019-01-03T02:12:32.351966
Evidence open (via page says license)
Journal Is Open Access false
Open Access Status hybrid
PDF URL https://www.biorxiv.org/content/biorxiv/early/2017/09/01/161547.full.pdf
Publisher URL https://doi.org/10.1101/161547