Next Article in Journal
Unsupervised Learning of Depth from Monocular Videos Using 3D-2D Corresponding Constraints
Next Article in Special Issue
Remote and Proximal Assessment of Plant Traits
Previous Article in Journal
Proximal Imaging of Changes in Photochemical Reflectance Index in Leaves Based on Using Pulses of Green-Yellow Light
Previous Article in Special Issue
Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow
Article

Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning

1
Center for Advanced Bioenergy and Bioproducts Innovation, Urbana, IL 61801, USA
2
Institute for Genomic Biology, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
3
USDA-ARS, Global Change & Photosynthesis Research Unit, Urbana, IL 61801, USA
4
Department of Plant Biology, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
5
Center for Digital Agriculture, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
6
Department of Crop Sciences, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Ittai Herrmann
Remote Sens. 2021, 13(9), 1763; https://doi.org/10.3390/rs13091763
Received: 19 February 2021 / Revised: 27 April 2021 / Accepted: 28 April 2021 / Published: 1 May 2021
(This article belongs to the Special Issue Remote and Proximal Assessment of Plant Traits)
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. View Full-Text
Keywords: unmanned aerial vehicles; high throughput phenotyping; machine learning; bioenergy crops unmanned aerial vehicles; high throughput phenotyping; machine learning; bioenergy crops
Show Figures

Graphical abstract

MDPI and ACS Style

Varela, S.; Pederson, T.; Bernacchi, C.J.; Leakey, A.D.B. Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning. Remote Sens. 2021, 13, 1763. https://doi.org/10.3390/rs13091763

AMA Style

Varela S, Pederson T, Bernacchi CJ, Leakey ADB. Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning. Remote Sensing. 2021; 13(9):1763. https://doi.org/10.3390/rs13091763

Chicago/Turabian Style

Varela, Sebastian, Taylor Pederson, Carl J. Bernacchi, and Andrew D.B. Leakey. 2021. "Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning" Remote Sensing 13, no. 9: 1763. https://doi.org/10.3390/rs13091763

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop