Abstract
Increasing the yield of perennial forage crops remains a crucial factor underpinning the profitability of grazing industries, and therefore is a priority for breeding programs. Breeding for high dry matter yield (DMY) in forage crops is likely to be enhanced with the development of genomic selection (GS) strategies. However, realising the full potential of GS will require an increase in the amount of phenotypic data and the rate at which it is collected. Therefore, phenotyping remains a critical bottleneck in the implementation of GS in forage species. Assessments of DMY in forage crop breeding include visual scores, sample clipping and mowing of plots, which are often costly and time-consuming. New ground- and aerial-based platforms equipped with advanced sensors offer opportunities for fast, nondestructive and low-cost, high-throughput phenotyping (HTP) of plant growth, development and yield in a field environment. The workflow of image acquisition, processing and analysis are reviewed. The “big data” challenges, proposed storage and management techniques, development of advanced statistical tools and methods for incorporating the HTP into forage breeding systems are also reviewed. Initial results where these techniques have been applied to forages have been promising but further research and development is required to adapt them to forage breeding situations, particularly with respect to the management of large data sets and the integration of information from spaced plants to sward plots. However, realizing the potential of sensor technologies combined with GS leads to greater rates of genetic gain in forages.
Highlights
Increasing the dry matter yield (DMY) of perennial forages remains a crucial factor underpinning the profitability of grazing industries [1], and is a priority outcome for forage breeding programs
Active sensors often possess a narrow field of view (FOV), whereas passive sensors are less limited to FOV depending on the light source distance from target objects
machine learning (ML) is an exciting technique that can be widely applied in forage data analysis for integration, interpretation and further quantification of phenotypic traits at large population field trials, those that may be required for genomic selection
Summary
Increasing the dry matter yield (DMY) of perennial forages remains a crucial factor underpinning the profitability of grazing industries [1], and is a priority outcome for forage breeding programs. Various sensors, vehicle platforms, data loggers and real-time kinematic global navigation and satellite systems (RTK-GNSS), along with processing pipelines, have been used to capture phenotypic data in the field from single plants, rows and sward plots of 270,000 perennial ryegrass genotypes [12]. Most of these have been demonstrated in the context of breeding programs, indicating the potential of utilising sensor-based technology for DMY phenotyping in forage improvement programs. We summarise the workflow of image processing and analysis as well as a method for modelling data postprocessing techniques
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