High‐Throughput Phenotyping of Cotton in Multiple Irrigation Environments

Abstract

Rapid screening of plant growth provides additional phenological information for cotton (Gossypium hirsutum L.), which may then be linked to productivity analysis in breeding and agronomy. We tested automated measurements of plant height, ground cover fraction (GCF), normalized difference vegetation index (NDVI), and canopy temperature (Tc) using a ground‐based platform mounted on a research sprayer on four cotton cultivars (DP1212, DP1219, FM2484, and PHY499) grown under 10 different irrigation levels at Lubbock, TX. Measurements consistently identified all irrigation treatments throughout the season in 2012, a dry year, and the lower irrigation treatments in 2013, a wet year, as well as differences in growth habits among cultivars in both years. The coefficient of determination (r2) of each in‐season parameter had a positive correlation with lint yield during both years. Lint yield values were compared with in‐season measurements at each sampling date. All cultivars had higher correlations between in‐season measurements and lint yield in 2012 than in 2013 because of the increased rainfall in 2013. In 2012, canopy temperature had highest correlation with lint yield, followed by plant height, GCF, and NDVI. In 2013, GCF, NDVI, and plant height all had higher correlations than Tc with lint yield. All the methods showed value in determining growth and yield, but selection of methods for screening may require adaptations based on the growth environment of the study.

The quest for understanding phenotypic variation via integrated approaches in the field environment

“…2). Further supporting the ability of FB-HTP to monitor plant development under managed stress, Sharma and Ritchie (2015) used a similar system to identify differential growth characteristics among cotton cultivars evaluated under varying irrigation levels. The novel ability of FB-HTP to capture the temporal changes of a phenotype provides the critical pivot needed to focus on developmental quantitative genetics in order to grasp how trait manifestation is a function of many QTL whose effects fluctuate with time (Wu et al, 1999).…”

In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR

“…These characteristics are useful not only for quantitative analysis of genotype-environment interactions, which is essential for increasing crop performance [3][4][5], but also for improving crop management strategies such as fertilization, irrigation, and optimization of harvesting [6][7][8]. Several studies have shown interactions between geometric parameters of plants and crop yield as well as biomass [3,5,[9][10][11]. Manual measurements of crop traits, which are still often used in practical phenomic applications, have significant limitations and drawbacks.…”

“…Manual measurements of crop traits, which are still often used in practical phenomic applications, have significant limitations and drawbacks. These methods are time-consuming and labor intensive, which inevitably increases cost [9,12]. Additionally, manually obtained measurements are subject to human error due to fatigue and distractions during data collection.…”

Using Normalized Difference Red Edge Index to Assess Maturity in Cotton

“…Vegetation indices have been primarily used to indicate the response of vegetative growth to biotic and abiotic stresses of several crops, including cotton (Falkenberg et al, 2007; Zhao et al, 2007; Sharma and Ritchie, 2015). However, the use of these indices to determine phenology of the crop have been limited by the inability of the sensors to specifically detect flowers and bolls on the plants.…”