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Application of Geographically Weighted Regression to Improve Grain Yield Prediction from Unmanned Aerial System Imagery
Author(s) -
Haghighattalab Atena,
Crain Jared,
Mondal Suchismita,
Rutkoski Jessica,
Singh Ravi Prakash,
Poland Jesse
Publication year - 2017
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2016.12.1016
Subject(s) - phenology , yield (engineering) , growing season , principal component analysis , vegetation (pathology) , grain yield , regression analysis , crop , linear regression , regression , satellite imagery , crop yield , agronomy , aerial imagery , remote sensing , biology , environmental science , physical geography , statistics , mathematics , geography , medicine , materials science , pathology , metallurgy
Phenological data are important ratings of the in‐season growth of 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.