Design of an Unmanned Ground Vehicle and LiDAR Pipeline for the High-Throughput Phenotyping of Biomass in Perennial Ryegrass

Phat Nguyen,Fan Shi,Hans D Daetwyler,Pieter E Badenhorst,Kevin F Smith,German C Spangenberg

doi:10.3390/rs13010020

Phat Nguyen, Fan Shi + Show 4 more

Open Access

https://doi.org/10.3390/rs13010020

Copy DOI

Journal: Remote sensing	Publication Date: Dec 23, 2020
Citations: 10	License type: CC BY 4.0

Affiliation: La Trobe University, Agriculture Victoria, AgriBio

Abstract

Perennial ryegrass biomass yield is an important driver of profitability for Australian dairy farmers, making it a primary goal for plant breeders. However, measuring and selecting cultivars for higher biomass yield is a major bottleneck in breeding, requiring conventional methods that may be imprecise, laborious, and/or destructive. For forage breeding programs to adopt phenomic technologies for biomass estimation, there exists the need to develop, integrate, and validate sensor-based data collection that is aligned with the growth characteristics of plants, plot design and size, and repeated measurements across the growing season to reduce the time and cost associated with the labor involved in data collection. A fully automated phenotyping platform (DairyBioBot) utilizing an unmanned ground vehicle (UGV) equipped with a ground-based Light Detection and Ranging (LiDAR) sensor and Real-Time Kinematic (RTK) positioning system was developed for the accurate and efficient measurement of plant volume as a proxy for biomass in large-scale perennial ryegrass field trials. The field data were collected from a perennial ryegrass row trial of 18 experimental varieties in 160 plots (three rows per plot). DairyBioBot utilized mission planning software to autonomously capture high-resolution LiDAR data and Global Positioning System (GPS) recordings. A custom developed data processing pipeline was used to generate a plant volume estimate from LiDAR data connected to GPS coordinates. A high correlation between LiDAR plant volume and biomass on a Fresh Mass (FM) basis was observed with the coefficient of determination of R2 = 0.71 at the row level and R2 = 0.73 at the plot level. This indicated that LiDAR plant volume is strongly correlated with biomass and therefore the DairyBioBot demonstrates the utility of an autonomous platform to estimate in-field biomass for perennial ryegrass. It is likely that no single platform will be optimal to measure plant biomass from landscape to plant scales; the development and application of autonomous ground-based platforms is of greatest benefit to forage breeding programs.

Highlights

For our row/plot field trial, we developed a specific program and graphical user interface to create missions from measured Global Positioning System (GPS) points to navigate the DairyBioBot on specific paths through a field trial (Figure 3)
The set-up cost of the autonomous vehicle is greater than mounting sensors on existing ground-based crewed vehicles, the DairyBioBot has the great advantage of having a low operational cost for long-term deployment, and this would be true if the sensors were mounted on pre-existing autonomous vehicles
Our results indicate that standalone Light Detection and Ranging (LiDAR) plant volume can be used to predict biomass for perennial ryegrass row and plot trials

Summary

Introduction

Perennial ryegrass (Lolium perenne L.) is the most important forage species in temperate pasture regions, such as Northern Europe, New Zealand, and Australia [1]. In Australia, perennial ryegrass is the dominant pasture grass utilized as the grazing feed-base in dairy and meat production livestock industries. These industries have an estimated economic gross value of AU $8 billion per year in Australia [2].

Methods

Discussion

Conclusion