In Situ Cotton Leaf Area Index by Height Using Three‐Dimensional Point Clouds

Abstract

Three‐dimensional (3‐D) high‐throughput crop phenotyping may benefit plant research and breeding programs by providing a rapid, nondestructive method of determining in‐season crop growth and development. In this study, a set of three inexpensive, structured, near‐infrared laser projectors mounted on a robotic platform were used to generate high‐density color point clouds (PCs) in cotton (Gossypium hirsutum L.) in 2016 and 2017. The PCs were calibrated based on destructive leaf area measurements, and the transformed PCs were analyzed to quantify leaf area by canopy height in 1‐cm intervals. We conducted weekly scans of 8‐m plots throughout the growing season for two cotton cultivars and two irrigation treatments during the 2 yr. The calibrated PCs were used to accurately measure leaf area index and leaf area index by canopy height. The analysis of leaf area distribution throughout the growing season indicated significant cultivar and irrigation effects by canopy height as well as significant interactions between cultivar and irrigation. Based on the results, it is possible to use a 3‐D sensor system to distinguish growth habits that are not easily measured using traditional growth measurements. The information obtained from 3‐D measurements may provide additional information about several canopy characteristics that are currently difficult to measure, including radiation capture parameters and biomass partitioning.Core Ideas Leaf area index (LAI) has not traditionally been separated in plants by height. Three‐dimensional sensing was used to accurately measure LAI by height in cotton. Cotton cultivar, irrigation, and growth stage measurably affected LAI by height. Three‐dimensional sensing may complement measures of radiation capture and other spatial factors.