From Phenotyping to Phenomics II – Translation to Agile Agritech Tools for Field Crop and Genebank Material Evaluation

A special issue of Plants (ISSN 2223-7747).

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 2103

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Plant Functional Biology, AgResearch, Mosgiel, New Zealand
Interests: plant phenotypic diversity; crops and forages; genetic resources; plant phenomics; plant phenotypic evolution
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Dear Colleagues,

The plant phenotype is a complex product driven by plant genetics, growth conditions, environmental factors and their interactions over generations. Whole plants can be phenotyped for their identity, biomass, dry weight or, in some cases, quality. Plant organs are also phenotyped for a trait in that organ or as an indicator of a trait for the whole plant. With the advent of better data analytical methods, machine learning and more accurate and lighter sensors, our understanding of the mechanisms of evolution in plant traits during growth has improved dramatically. The epigenetic regulations during growth also impact phenotypes over several generations of germplasm growth in the field. Despite all this progress, there are still challenging traits that are extremely difficult to measure in crop, forage and tree species. Continuous measurement of these traits in the field and under differing conditions would help the advancement of these technologies into reliable field-based agritech tools. This Special Issue in Plants will provide insights into the advances in plant phenomics methods and tools, their transferability to the field for large-scale germplasm phenotyping and how environmental factors influence these tools and methods in the field.

Dr. Kioumars Ghamkhar
Guest Editor

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Research

19 pages, 4554 KiB  
Article
UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies
by Karsten M. E. Nielsen, Hema S. N. Duddu, Kirstin E. Bett and Steve J. Shirtliffe
Plants 2022, 11(20), 2691; https://doi.org/10.3390/plants11202691 - 12 Oct 2022
Viewed by 1641
Abstract
Plant growth rate is an essential phenotypic parameter for quantifying potential crop productivity. Under field conditions, manual measurement of plant growth rate is less accurate in most cases. Image-based high-throughput platforms offer great potential for rapid, non-destructive, and objective estimation of plant growth [...] Read more.
Plant growth rate is an essential phenotypic parameter for quantifying potential crop productivity. Under field conditions, manual measurement of plant growth rate is less accurate in most cases. Image-based high-throughput platforms offer great potential for rapid, non-destructive, and objective estimation of plant growth parameters. The aim of this study was to assess the potential for quantifying plant growth rate using UAV-based (unoccupied aerial vehicle) imagery collected multiple times throughout the growing season. In this study, six diverse lines of lentils were grown in three replicates of 1 m2 microplots with six biomass collection time-points throughout the growing season over five site-years. Aerial imagery was collected simultaneously with each manual measurement of the above-ground biomass time-point and was used to produce two-dimensional orthomosaics and three-dimensional point clouds. Non-linear logistic models were fit to multiple data collection points throughout the growing season. Overall, remotely detected vegetation area and crop volume were found to produce trends comparable to the accumulation of dry weight biomass throughout the growing season. The growth rate and G50 (days to 50% of maximum growth) parameters of the model effectively quantified lentil growth rate indicating significant potential for image-based tools to be used in plant breeding programs. Comparing image-based groundcover and vegetation volume estimates with manually measured above-ground biomass suggested strong correlations. Vegetation area measured from a UAV has utility in quantifying lentil biomass and is indicative of leaf area early in the growing season. For mid- to late-season biomass estimation, plot volume was determined to be a better estimator. Apart from traditional traits, the estimation and analysis of plant parameters not typically collected in traditional breeding programs are possible with image-based methods, and this can create new opportunities to improve breeding efficiency mainly by offering new phenotypes and affecting selection intensity. Full article
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