Real-time, non-destructive and in-field foliage yield and growth rate measurement in perennial ryegrass (Lolium perenne L.)

Abstract

Background In-field measurement of yield and growth rate in pasture species is imprecise and costly, limiting scientific and commercial application. Our study proposed a LiDAR-based mobile platform for non-invasive vegetative biomass and growth rate estimation in perennial ryegrass ( Lolium perenne L.). This included design and build of the platform, development of an algorithm for volumetric estimation, and field validation of the system. The LiDAR-based volumetric estimates were compared against fresh weight and dry weight data across different ages of plants, seasons, stages of regrowth, sites, and row configurations. Results The project had three phases, the last one comprising four experiments. Phase 1: a LiDAR-based, field-ready prototype mobile platform for perennial ryegrassrecognition in single row plots was developed. Phase 2: real-time volumetric data capture, modelling and analysis software were developed and integrated and the resultant algorithm was validated in the field. Phase 3. LiDAR Volume data were collected via the LiDAR platform and field-validated in four experiments. Expt.1: single-row plots of cultivars and experimental diploid breeding populations were scanned in the southern hemisphere spring for biomass estimation. Significant ( P < 0.001) correlations were observed between LiDAR Volume and both fresh and dry weight data from 360 individual plots (R 2 = 0.89 and 0.86 respectively). Expt 2: recurrent scanning of single row plots over long time intervals of a few weeks was conducted, and growth was estimated over an 83 day period. Expt 3: recurrent scanning of single-row plots over nine short time intervals of 2 to 5 days was conducted, and growth rate was observed over a 26 day period. Expt 4: recurrent scanning of paired-row plots over an annual cycle of repeated growth and defoliation was conducted, showing an overall mean correlation of LiDAR Volume and fresh weight of R 2 = 0.79 for 1008 observations made across seven different harvests between March and December 2018. Conclusions Here we report development and validation of LiDAR-based volumetric estimation as an efficient and effective tool for measuring fresh weight, dry weight and growth rate in single and paired-row plots of perennial ryegrass for the first time, with a consistently high level of accuracy. This development offers precise, non-destructive and cost-effective estimation of these economic traits in the field for ryegrass and potentially other pasture grasses in the future, based on the platform and algorithm developed for ryegrass. Electronic supplementary material The online version of this article (10.1186/s13007-019-0456-2) contains supplementary material, which is available to authorized users.

Divergent Genomic Selection for Herbage Accumulation and Days-To-Heading in Perennial Ryegrass

“…Future optimization of genomic selection should focus on increasing the training set size and a careful consideration of its composition [22] with regards to relatedness to the intended selection population(s). Expansion of the size of training sets for DM yield should be possible, with the advent of non-destructive sensor-based tools for measuring DM yield in plots [47,48], set to overcome phenotyping bottlenecks.…”

Evaluation of LiDAR scanning for measurement of yield in perennial ryegrass

“…All harvested herbage was removed from the trial site. LiDAR raw data were analysed using a software processing package that converts raw data to a unitless volumetric index (LiDAR volume, LV) via a developed algorithm by Ghamkhar et al (2019). In the field, the LiDAR capture software parameters were set to: row length = 36 m; segments per row = 18; row spacing = 550 mm; and cutting height = 40 mm.…”

Development and Validation of a Phenotyping Computational Workflow to Predict the Biomass Yield of a Large Perennial Ryegrass Breeding Field Trial

“…Increasing biomass yield is the most important trait to improve during the breeding of perennial ryegrass (Smith et al, 2001;McDonagh et al, 2016;Herridge et al, 2018;Ghamkhar et al, 2019). However, biomass yield is a complex trait, which varies with the number and density of tillers, regrowth after defoliation and growth habit; across a range of seasons, environments, and plant age (Yates et al, 2019).…”

“…However, biomass yield is a complex trait, which varies with the number and density of tillers, regrowth after defoliation and growth habit; across a range of seasons, environments, and plant age (Yates et al, 2019). Furthermore, the early stages of perennial ryegrass breeding programs depend on the assessment of populations based on large numbers of genotypes planted as spaced plants or small sward plots in the field (Lootens et al, 2016;Ghamkhar et al, 2019). These assessments involve multiple measurement and selection procedures across seasons and years to repeatedly evaluate biomass yield (Leddin et al, 2018).…”

“…This contributes to the slow process of new cultivar development and commercialization. The application of sensorbased high-throughput phenotyping (HTP) technologies on aerial and ground-based mobile platforms have the potential to offer a non-destructive, rapid and efficient method to assess biomass yield in the field (Inostroza et al, 2016;Gebremedhin et al, 2019b;Ghamkhar et al, 2019).…”