Image‐based high‐throughput phenotyping for the estimation of persistence of perennial ryegrass (Lolium perenne L.)—A review. (10th February 2021) - Jayasinghe, Chinthaka; Badenhorst, Pieter; Jacobs, Joe; Spangenberg, German; Smith, Kevin
Abstract: Perennial ryegrass ( Lolium perenne L.) is considered the most important pasture species in temperate agriculture, with over six million hectares of sown area in Australia alone. However, perennial ryegrass has poor persistence in some environments because of low tolerance to a range of both abiotic and biotic stresses. To breed perennial ryegrass, cultivars with greater persistence and productivity may require evaluation of genotypes over a number of years. Persistence assessment in pasture breeding depends on manual ground cover estimation or counting the number of surviving plants or tillers in a known area. These methods are subjective and labour intensive, which may limit data collection in large‐scale breeding programs. With the rapid development of sensors and image processing algorithms, image‐based high‐throughput phenotyping (HTP) is becoming commonplace in the breeding of major food crops. Image‐based HTP approaches consist of the deployment of a wide range of sensors on ground‐based or airborne platforms and data analysed through image processing pipelines. Image‐based HTP shows high potential for use in pasture phenotyping in breeding programs and may be able to reduce timeframes for releasing new cultivars. Moreover, existing image‐based HTP approaches could be further developed to include precise tools for phenotyping pasture persistence traits such as pasture senescence, botanical composition, pathogen and pest resistance. In this study, we reviewed existing image‐based HTP approaches in precision agriculture and discussed their feasibility for perennial ryegrass persistence estimation in pasture breeding. Although the paper focuses on application in perennial ryegrass, the principles equally apply to other perennial forage species.