Assessing spatio-temporal patterns of sugarcane aphid (Hemiptera: Aphididae) infestations on silage sorghum yield using unmanned aerial systems (UAS)

Abstract

In the U.S. since 2013, the sugarcane aphid is a perennial pest to all types of sorghum. Rating sugarcane aphid population density, plant damage, and other traits in sorghum requires a large amount of labor and ratings, especially damage ratings, may vary by evaluator. Thus Unmanned Aerial Systems (UAS)–based imagery may be exceedingly useful to more accurately quantify the effects on sorghum caused by sugarcane aphids. This study quantified the dynamic nature of sugarcane aphid infestations on silage sorghum varieties using UAS-based imagery data, and demonstrated the UAS-based measurements correlated to ground measurements. Two UAS platforms equipped with RGB (red, green, and blue) and multispectral cameras respectively were used to evaluate the silage sorghum variety trials during the growing seasons of 2019 and 2020. For the purpose of high throughput phenotyping in sorghum breeding, a new workflow scheme was developed including UAS image processing, raster calculation, DTM (digital terrain model) and CHM (canopy height model) generation, image extraction of sorghum plants, and tabular dataset generation from zonal statistics for further statistical analyses. Ground-based measurements included aphid sampling, aphid damage ratings, plant height, and biomass yields. The normalized difference red edge index (NDRE) and canopy cover collected by the UAS showed negative linear relationship with aphid damage ratings in both trials (R2 = 0.55–0.64). In addition to assessing spatial differences among the varieties in 2019, temporal change in both NDRE and canopy cover from the baseline sampling date in 2020 better estimated aphid damage, R2 of 0.68 and 0.79 respectively, than using the spatial difference of NDRE (R2 = 0.55) and canopy cover (R2 = 0.57) before harvest. Plant height (R2 = 0.84, Root-Mean-Square Error (RMSE) = 0.16 m) can be estimated with efficiency and precision using UAS-derived measurements during high throughput phenotyping of sorghum. Fresh yield estimates for the primary harvests were consistent in both years, but green yield estimates differed among harvests and need to be improved. Future development of UAS-based high throughput phenotyping would benefit from increased temporal resolutions of growth parameters and vegetation indices throughout a growing season.