Open AccessEvaluation of LiDAR scanning for measurement of yield in perennial ryegrass
Author(s) -
Richard George,
Brent Barrett,
Kioumars Ghamkhar
Publication year - 2019
Publication title -
journal of new zealand grasslands
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 2
eISSN - 2463-2880
pISSN - 2463-2872
DOI - 10.33584/jnzg.2019.81.414
Subject(s) - perennial plant , yield (engineering) , pasture , dry matter , bottleneck , lidar , agronomy , environmental science , growing season , scanner , biology , remote sensing , geography , computer science , materials science , metallurgy , embedded system , artificial intelligence
mproving pasture yields is a primary goal for plant breeders. However, measuring and selecting for yield is a major bottleneck in breeding, requiring methods that are laborious, destructive, and/or imprecise. A computerised scanner developed in Canterbury using LiDAR (light detection and ranging) technology was evaluated in the Waikato on perennial ryegrass paired-row breeding plots. At eight timepoints, all plots were scanned prior to mechanical defoliation and recording of fresh weight (FW) and dry matter (DM) yield on a random subset of plots. Yield data on 1206 FW and 504 DM samples were compared with LiDAR scan results on a seasonal basis by regression. Winter, spring, summer and autumn correlation with FW were R2 = 0.81, 0.92, 0.94 and 0.90, respectively, and with DM yield R2 = 0.87, 0.73, 0.87 and 0.79, respectively. These results indicate LiDAR estimation of DM yield was accurate within seasons for the paired-row breeding plots, although it was sensitive to large changes in dry matter content (%) among seasons, which may require seasonal algorithms to correct for this variation if this technology is to be adopted. In conclusion, the scanner could be useful in removing a major bottleneck in perennial ryegrass breeding and may have application for agronomy and farm management in cases where precise non-destructive real-time estimation of DM yield are of value.