Plant Science Innovation, Center for
ORCID IDs
https://orcid.org/0000-0001-8960-094X
https://orcid.org/0000-0002-4442-6578
https://orcid.org/0000-0001-6739-5527
Document Type
Article
Date of this Version
2020
Citation
Kenchanmane Raju, S. K., A. M. Thompson, and J. C. Schnable. 2020. Advances in plant phenomics: From data and algorithms to biological insights. Applications in Plant Sciences 8(8): doi:10.1002/aps3.11386
Abstract
The measurement of the characteristics of living organisms is re- ferred to as phenotyping (Singh et al., 2016). While the use of phe- notyping in plant biology and genetics can be traced back at least to Gregor Mendel sorting and counting peas by shape and pod color 160 years ago, addressing current questions in plant biology, genet- ics, and breeding often requires increasingly precise phenotyping of a wide range of traits. Accurate phenotyping has played a role in both novel discoveries about the fundamental biology of plants and the development of improved crop varieties around the world. With the advent of inexpensive genotyping tools, crop functional genomics has entered the “big data” era, but efficient large-scale phenotyping is still an impediment hindering plant functional genomics. The precise measurement of plant traits both throughout the growth cycle and across environments is expensive and labor intensive. A convergence of interdisciplinary efforts has led to the development of new technologies for nondestructive phenotyping in plants to measure large numbers of traits accurately with higher throughput (Close and Last, 2011). Improvements in imaging and automation, as well as in data processing and analytics, are helping to fill significant gaps in efforts to employ these new technologies to connect genetic variation with phenotypes (Yang et al., 2020). In recent years, plant phenomics research has transitioned from the development of methods and molecular genetic analysis of model plants in controlled environments toward accelerated efforts for applications in plant breeding, association studies, and stress phenotyping in crops grown under complex field conditions (Costaet al., 2018). In this special issue, “Advances in Plant Phenomics: From Data and Algorithms to Biological Insights,” we present six papers that capture plant phenomics extending to multiple scales, from field-wide traits, to individual plots or plants, to specific gene interactions.
In the context of field-scale image acquisition and processing, one of the first challenges that must be addressed in drone-based imaging of agricultural fields is turning free-flown images acquired over an area into a single mosaic image from which phenotypes can be extracted. Current methods rely mostly on the ability to locate each pixel in space, requiring costly global positioning systems (GPS) and/or inertial measurement units (IMU) to track the posi- tion of ground control points relative to the image acquisition de- vice. These approaches are computationally taxing, demand larger data storage, and require the purchase of software licenses, lead- ing to a high barrier of entry. Aktar et al. (2020) have developed a method called Video Mosaicking and summariZation (VMZ) to provide an alternative pipeline that is faster, less computationally de- manding, and much cheaper to implement. The authors show that compared to other methods, VMZ not only works faster but also produces mosaics with superior quality. This work, demonstrated here in maize, begins to democratize drone-based phenotyping for large- and small-scale field researchers across multiple species.
Comments
2020 Authors