Identification of Selection Preferences and Predicting Yield Related Traits in Sugarcane Seedling Families Using RGB Spectral Indices

James Todd (), Richard Johnson, David Verdun and Katie Richard
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James Todd: Sugarcane Research Unit, USDA-ARS, 5883 USDA Rd., Houma, LA 70360, USA
Richard Johnson: Sugarcane Research Unit, USDA-ARS, 5883 USDA Rd., Houma, LA 70360, USA
David Verdun: Sugarcane Research Unit, USDA-ARS, 5883 USDA Rd., Houma, LA 70360, USA
Katie Richard: Sugarcane Research Unit, USDA-ARS, 5883 USDA Rd., Houma, LA 70360, USA

Agriculture, 2022, vol. 12, issue 9, 1-16

Abstract: The early stages of the United States Department of Agriculture (USDA) Louisiana commercial sugarcane breeding program involve planting large numbers of genetically unique seedlings that require time and resources to evaluate. Selection is made quickly, is subjective, and related to the appearance of yield and vigor. Remote sensing techniques have been used to predict yield of several crops over large areas using areal images. To understand selection preferences better and if remote sensing techniques could be used to increase efficiency, twelve sugarcane seedling families each having approximately 263 seedlings were planted in two replications at the USDA-ARS Ardoyne farm. Stalk height, number and diameter ratings were taken on 50 stools of each replication of each family. Red-Green-Blue images were taken of the seedling field in plant cane and first ratoon before selection. Spectral indices were derived from the images for each plot. Height had the largest influence on visual selections of the field measurements evaluated. Several spectral indices such as the Green Area (GA) correlated highly with important traits including Height (>0.80), selection rates (>0.70), and Brix (>0.60). The results show the potential for seedling evaluation by remote sensing methods.

Keywords: breeding; remote sensing; RGB; CIELab (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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