Abstract
Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.
Highlights
In the last 20 years, nucleic acid sequencing techniques have driven major advances in crop genomics, genetics and molecular biology [1,2]
This study successfully addressed its goals by demonstrating that early- and mid-season measurement of dynamic growth traits by Unmanned aerial vehicles (UAV) can be combined with individual timepoint trait data to facilitate improved prediction of end-of-season above-ground biomass (AGB) (Figures 6 and 7)
Spectral information has proved to be an important proxy for AGB prediction in annual grain crops in these studies, but prediction accuracy significantly increased when integrating both types of traits in the same model [54,56]
Summary
In the last 20 years, nucleic acid sequencing techniques have driven major advances in crop genomics, genetics and molecular biology [1,2]. Recent technological advances provide new opportunities for the use of unmanned aerial vehicles (UAV) as a low-cost platform for carrying sensors that will deliver high spatial, temporal, and spectral resolution imagery to generate precise information about the interaction of solar radiation and vegetation [5]. UAV-based structurefrom-motion (SfM) techniques have been rapidly adopted to estimate traits such as canopy height [6,7] and yield [8]. Remote sensing from multispectral and hyperspectral sensors and image analysis techniques have been utilized to monitor nutrient status [9,10], aboveground biomass (AGB) [11,12], leaf area index [13,14], canopy cover [15], and senescence rate [15,16]. Most analyses have been limited to one, or a small number, of sampling dates that are often focused towards the end of the growing season
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
