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.

中文翻译：

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基于图像的高通量表型评估多年生黑麦草 (Lolium perenne L.) 的持久性——综述

多年生黑麦草（Lolium perenneL.) 被认为是温带农业中最重要的牧场物种，仅在澳大利亚就有超过 600 万公顷的播种面积。然而，由于对一系列非生物和生物胁迫的耐受性低，多年生黑麦草在某些环境中的持久性较差。为了培育多年生黑麦草，具有更高持久性和生产力的栽培品种可能需要对基因型进行数年评估。牧场育种中的持久性评估取决于手动地面覆盖估计或计算已知区域内幸存植物或分蘖的数量。这些方法是主观的和劳动密集型的，这可能会限制大规模育种计划中的数据收集。随着传感器和图像处理算法的飞速发展，基于图像的高通量表型 (HTP) 在主要粮食作物的育种中变得司空见惯。基于图像的 HTP 方法包括在地面或机载平台上部署各种传感器以及通过图像处理管道分析的数据。基于图像的 HTP 显示出在育种计划中用于牧场表型的巨大潜力，并且可能能够减少发布新品种的时间范围。此外，可以进一步开发现有的基于图像的 HTP 方法，以包括用于对牧场持久性状进行表型分析的精确工具，例如牧场衰老、植物成分、病原体和害虫抗性。在这项研究中，我们回顾了精准农业中现有的基于图像的 HTP 方法，并讨论了它们在牧草育种中估计多年生黑麦草持久性的可行性。