Application of geographically weighted regression to improve grain yield prediction from unmanned aerial system imagery

Atena Haghighattalab, Jared Crain, Suchismita Mondal, Jessica Rutkoski, Ravi Prakash Singh, Jesse Poland

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Phenological data are important ratings of the in-seasongrowthof crops, though this assessment is generally limited at both spatial and temporal levels during the crop cycle for large breeding nurseries. Unmanned aerial systems (UAS) have the potential to provide high spatial and temporal resolution for phenotyping tens of thousands of small field plots without requiring substantial investments in time, cost, and labor. The objective of this research was to determine whether an accurate remote sensing-based method could be developed to estimate grain yield using aerial imagery in small-plot wheat (Triticum aestivum L.) yield evaluation trials. The UAS consisted of a modified consumer-grade camera mounted on a low-cost unmanned aerial vehicle and was deployed multiple times throughout the growing season in yield trials of advanced breeding lines with irrigated and drought-stressed environments at the International Maize and Wheat Improvement Center in Ciudad Obregon, Sonora, Mexico. We assessed data quality and evaluated the potential to predict grain yield on a plot level by examining the relationships between information derived from UAS imagery and the grain yield. Using geographically weighted (GW) models, we predicted grain yield for both environments. The relationship between measured phenotypic traits derived from imagery and grain yield was highly correlated (r = 0.74 and r = 0.46 [p < 0.001] for drought and irrigated environments, respectively). Residuals from GW models were lower and less spatially dependent than methods using principal component regression, suggesting the superiority of spatially corrected models. These results show that vegetation indices collected from high-throughput UAS imagery can be used to predict grain and for selection decisions, as well as to enhance genomic selection models.

Original languageEnglish (US)
Pages (from-to)2478-2489
Number of pages12
JournalCrop Science
Volume57
Issue number5
DOIs
StatePublished - 2017
Externally publishedYes

Bibliographical note

Funding Information:
This work was done through the International Maize and Wheat Improvement Center (CIMMYT), Mexico, and was supported by the National Science Foundation under Grant no. IOS-1238187, and through support provided by Feed the Future through the US Agency for International Development, under the terms of Contract no. AID-OAA-A-13-00051. The authors would like to thank three anonymous reviewers for their assistance in improving the manuscript. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or of the US Agency for International Development.

Publisher Copyright:
© Crop Science Society of America.

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