Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling

Seth A Tolley; Neal Carpenter; Melba M Crawford; Edward J Delp; Ayman Habib; Mitchell R Tuinstra

doi:10.3389/fpls.2023.1202536

Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling

Front Plant Sci. 2023 Jun 20:14:1202536. doi: 10.3389/fpls.2023.1202536. eCollection 2023.

Authors

Seth A Tolley¹, Neal Carpenter², Melba M Crawford^{1

3}, Edward J Delp⁴, Ayman Habib³, Mitchell R Tuinstra¹

Affiliations

¹ Department of Agronomy, Purdue University, West Lafayette, IN, United States.
² Analytics and Pipeline Design, Bayer Crop Science, Chesterfield, MO, United States.
³ Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, United States.
⁴ School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States.

Abstract

Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.

Keywords: RGB; UAV; border effect; high-throughput phenotyping; hyperspectral; lidar; plot trimming; remote sensing.

Grants and funding

This project was partially funded by grants from the Heat Tolerant Maize for Asia project supported by the Feed the Future Initiative of USAID organized by the International Maize and Wheat Improvement Center (CIMMYT) (Grant no. 15100072), the U.S. Department of Energy ARPA-E Program (Award Number DE-AR0001135), and the USDA National Institute of Food and Agriculture Hatch Program (Number 1023186).