Image‐based high‐throughput phenotyping for the estimation of persistence of perennial ryegrass (Lolium perenneL.)—A review

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.

Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review

“…The above papers, as well as papers [33,34,35,36], [37,38,39,40,41,42], mentioned the importance of object counting using ML and/or DL in agriculture. However, none of the papers provides an overview or an analysis of the counting methods.…”

Development of 3‐D point cloud imaging and 2‐D image processing methods for high‐throughput phenotyping of perennial ryegrass spikes

“…The scanning process for a single spike was completed in about 2 min depending on environmental factors mentioned previously. Under the right conditions and with experience, the scan time for a single spike was reduced to about 30 s. The application of image‐based HTP in precision agriculture has faced challenges due to limitations in image processing and sensors, but technological advancements in HTP approaches provide promising opportunities for pasture phenotyping, enabling the development of precise sensor‐based tools for evaluating plant traits and accelerating plant breeding programs (Jayasinghe et al., 2021). This unique HTP approach to measure the morphological differences related to seed retention using the 3‐D optical sensors of the Artec Space Spider provided high‐fidelity structural data, much of which has not been accurately obtained using traditional methods.…”

Utilising commercial farm grass growth data to evaluate long-term dry matter production of perennial ryegrass varieties

“…Utilising the current methodology, analysis which includes more varieties would be beneficial as would an extended time period of analysis for the current varieties. Developments in imaging technology may provide another solution to the problem of accurately measuring pasture persistence in the future (Jayasinghe et al, 2021); such methods would remove subjective differences between human assessors of GS. Trials which could examine in detail the interactions between farm management practices and variety selection over time may be most informative on the question of PRG variety persistence differences (Edmond, 1964;Wilkins & Humphreys, 2003).…”

“…Due to the costs of reseeding pasture, large economic weighting is given to variety persistence in variety ranking indexes, such as the Pasture Profit Index (PPI) in Ireland where persistency accounts for 29% of the total value given to a variety (Tubritt et al, 2021). Currently, persistence measurements are centred on the theory that the number of PRG tillers, or the percentage of ground covered by PRG tillers, and the annual changes in tiller numbers can influence the long-term productivity of PRGbased swards (Creighton, 2012;Jayasinghe et al, 2021). While this theory may have some value in short-term trials, there is no evidence available to show that the extrapolation of ground score (GS) data can predict the lifetime DM production of a variety.…”