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Open AccessArticle

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

1
Agriculture Victoria, Hamilton Centre, Hamilton, Victoria 3300, Australia
2
School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, the University of Melbourne, Victoria 3010, Australia
3
Agriculture Victoria, Ellinbank Centre, Ellinbank, Victoria 3821, Australia
4
Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia
5
School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3086, Australia
*
Author to whom correspondence should be addressed.
Agronomy 2020, 10(8), 1206; https://doi.org/10.3390/agronomy10081206
Received: 14 July 2020 / Revised: 3 August 2020 / Accepted: 14 August 2020 / Published: 17 August 2020
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. View Full-Text
Keywords: perennial ryegrass persistence; ground cover; pasture senescence; phenomics; hyperspectral data analysis perennial ryegrass persistence; ground cover; pasture senescence; phenomics; hyperspectral data analysis
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MDPI and ACS Style

Jayasinghe, C.; Badenhorst, P.; Jacobs, J.; Spangenberg, G.; Smith, K. High-Throughput Ground Cover Classification of Perennial Ryegrass (Lolium Perenne L.) for the Estimation of Persistence in Pasture Breeding. Agronomy 2020, 10, 1206. https://doi.org/10.3390/agronomy10081206

AMA Style

Jayasinghe C, Badenhorst P, Jacobs J, Spangenberg G, Smith K. High-Throughput Ground Cover Classification of Perennial Ryegrass (Lolium Perenne L.) for the Estimation of Persistence in Pasture Breeding. Agronomy. 2020; 10(8):1206. https://doi.org/10.3390/agronomy10081206

Chicago/Turabian Style

Jayasinghe, Chinthaka; Badenhorst, Pieter; Jacobs, Joe; Spangenberg, German; Smith, Kevin. 2020. "High-Throughput Ground Cover Classification of Perennial Ryegrass (Lolium Perenne L.) for the Estimation of Persistence in Pasture Breeding" Agronomy 10, no. 8: 1206. https://doi.org/10.3390/agronomy10081206

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