Browsing UA Faculty Research by Subjects
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Comparing Nadir and Multi-Angle View Sensor Technologies for Measuring in-Field Plant Height of Upland CottonPlant height is a morphological characteristic of plant growth that is a useful indicator of plant stress resulting from water and nutrient deficit. While height is a relatively simple trait, it can be difficult to measure accurately, especially in crops with complex canopy architectures like cotton. This paper describes the deployment of four nadir view ultrasonic transducers (UTs), two light detection and ranging (LiDAR) systems, and an unmanned aerial system (UAS) with a digital color camera to characterize plant height in an upland cotton breeding trial. The comparison of the UTs with manual measurements demonstrated that the Honeywell and Pepperl+Fuchs sensors provided more precise estimates of plant height than the MaxSonar and db3 Pulsar sensors. Performance of the multi-angle view LiDAR and UAS technologies demonstrated that the UAS derived 3-D point clouds had stronger correlations (0.980) with the UTs than the proximal LiDAR sensors. As manual measurements require increased time and labor in large breeding trials and are prone to human error reducing repeatability, UT and UAS technologies are an efficient and effective means of characterizing cotton plant height.
Innovations to expand drone data collection and analysis for rangeland monitoringIn adaptive management of rangelands, monitoring is the vital link that connects management actions with on-the-ground changes. Traditional field monitoring methods can provide detailed information for assessing the health of rangelands, but cost often limits monitoring locations to a few key areas or random plots. Remotely sensed imagery, and drone-based imagery in particular, can observe larger areas than field methods while retaining high enough spatial resolution to estimate many rangeland indicators of interest. However, the geographic extent of drone imagery products is often limited to a few hectares (for resolution ≤1 cm) due to image collection and processing constraints. Overcoming these limitations would allow for more extensive observations and more frequent monitoring. We developed a workflow to increase the extent and speed of acquiring, processing, and analyzing drone imagery for repeated monitoring of two common indicators of interest to rangeland managers: vegetation cover and vegetation heights. By incorporating a suite of existing technologies in drones (real-time kinematic GPS), data processing (automation with Python scripts, high performance computing), and cloud-based analysis (Google Earth Engine), we greatly increased the efficiency of collecting, analyzing, and interpreting high volumes of drone imagery for rangeland monitoring. End-to-end, our workflow took 30 d, while a workflow without these innovations was estimated to require 141 d to complete. The technology around drones and image analysis is rapidly advancing which is making high volume workflows easier to implement. Larger quantities of monitoring data will significantly improve our understanding of the impact management actions have on land processes and ecosystem traits. © 2021 The Authors.