An Object-Based Image Analysis Approach to Assess Persistence of Perennial Ryegrass (Lolium perenne L.) in Pasture Breeding

Abstract

Perennial ryegrass (Lolium perenne L.) is one of the most important forage grass species in temperate regions of the world, but it is prone to having poor persistence due to the incidence of abiotic and biotic stresses. This creates a challenge for livestock producers to use their agricultural lands more productively and intensively within sustainable limits. Breeding perennial ryegrass cultivars that are both productive and persistent is a target of forage breeding programs and will allow farmers to select appropriate cultivars to deliver the highest profitability over the lifetime of a sward. Conventional methods for the estimation of pasture persistence depend on manual ground cover estimation or counting the number of surviving plants or tillers in a given area. Those methods are subjective, time-consuming and/or labour intensive. This study aimed to develop a phenomic method to evaluate the persistence of perennial ryegrass cultivars in field plots. Data acquisition was conducted three years after sowing to estimate the persistence of perennial ryegrass using high-resolution aerial-based multispectral and ground-based red, green and blue(RGB) sensors, and subsequent image analysis. There was a strong positive relationship between manual ground cover and sensor-based ground cover estimates (p < 0.001). Although the manual plant count was positively correlated with sensor-based ground cover (p < 0.001) intra-plot plant size variation influenced the strength of this relationship. We conclude that object-based ground cover estimation is most suitable for use in large-scale breeding programs due to its higher accuracy, efficiency and repeatability. With further development, this technique could be used to assess temporal changes of perennial ryegrass persistence in experimental studies and on a farm scale.