High-Throughput Ground Cover Classification of Perennial Ryegrass (Lolium Perenne L.) for the Estimation of Persistence in Pasture Breeding

Chinthaka Jayasinghe,Kevin Smith,Pieter Badenhorst,Joe Jacobs,German Spangenberg

doi:10.3390/agronomy10081206

Chinthaka Jayasinghe, Kevin Smith + Show 3 more

Open Access

https://doi.org/10.3390/agronomy10081206

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Abstract

Perennial ryegrass (Lolium perenne L.) is one of the most important forage grass species in temperate regions of Australia and New Zealand. However, it can have poor persistence due to a low tolerance to both abiotic and biotic stresses. A major challenge in measuring persistence in pasture breeding is that the assessment of pasture survival depends on ranking populations based on manual ground cover estimation. Ground cover measurements may include senescent and living tissues and can be measured as percentages or fractional units. The amount of senescent pasture present in a sward may indicate changes in plant growth, development, and resistance to abiotic and biotic stresses. The existing tools to estimate perennial ryegrass ground cover are not sensitive enough to discriminate senescent ryegrass from soil. This study aimed to develop a more precise sensor-based phenomic method to discriminate senescent pasture from soil. Ground-based RGB images, airborne multispectral images, ground-based hyperspectral data, and ground truth samples were taken from 54 perennial ryegrass plots three years after sowing. Software packages and machine learning scripts were used to develop a pipeline for high-throughput data extraction from sensor-based platforms. Estimates from the high-throughput pipeline were positively correlated with the ground truth data (p < 0.05). Based on the findings of this study, we conclude that the RGB-based high-throughput approach offers a precision tool to assess perennial ryegrass persistence in pasture breeding programs. Improvements in the spatial resolution of hyperspectral and multispectral techniques would then be used for persistence estimation in mixed swards and other monocultures.

Highlights

Perennial ryegrass (Lolium perenne L.) has become the most widely sown perennial forage grass in temperate regions due to its high productivity, nutritive value, and ability to tolerate a range of grazing practices
Improvements in the spatial resolution of hyperspectral and multispectral techniques would be used for persistence estimation in mixed swards and other monocultures
Visual scorings for ground cover classification could be replaced by machine leaning (ML)-based image analysis pipeline or hyperspectral sensors without a significant loss of precision

Summary

Introduction

Perennial ryegrass (Lolium perenne L.) has become the most widely sown perennial forage grass in temperate regions due to its high productivity, nutritive value, and ability to tolerate a range of grazing practices. It may have poor persistence due to its low tolerance to both abiotic and biotic stresses [1]. Differences in persistence between perennial ryegrass cultivars may arise from variations in tolerance to abiotic and biotic stresses such as drought, heat, pests, and diseases. Agronomy 2020, 10, 1206 sward may depend on the number of plants per unit area and the size of individual plants [2]. The size of individual plants is influenced by the number of living tillers per plant and the size of the individual tillers. Tiller density of a sward can decline with time and gradually expose bare ground, which in turn provides opportunities for invading weed species to germinate and colonize. Tiller density decline will reduce the productivity of the sward in subsequent years

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