Identification of selection preferences and predicting tield related traits in sugarcane seedling families using RGB spectral indices

Authors: Todd, James; Johnson, Richard; Verdun, David; Richard, Katie

Submitted to: Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/25/2022
Publication Date: 8/26/2022
Citation: Todd, J.R., Johnson, R.M., Verdun, D.L., Richard, K.A. 2022. Identification of selection preferences and predicting tield related traits in sugarcane seedling families using RGB spectral indices. Agriculture. 12(9):1313. https://doi.org/10.3390/agriculture12091313.
DOI: https://doi.org/10.3390/agriculture12091313

Interpretive Summary: The commercial sugarcane breeding program develops new sugarcane cultivars for Louisiana. The breeding process requires significant inputs of both time and labor and involves the evaluation of thousands of plants every year. Remote sensing techniques show promise in estimating yields and evaluating plants over large areas in short time frames. To determine the potential of remote sensing to aid in selection in the labor-intensive early stages of the program, a drone equipped with a Red-Green-Blue camera was flown over replicated seedling plots. Height and stalk number measurements were taken on 50 plants per family and Brix on 20 plants per family. Families were also rated on performance traits. Several spectral indices were highly correlated with important traits including height, selection rates, and brix. The results show that remote sensing methods could reduce the labor requirements needed for seedling evaluation while maintaining the accuracy of the selection process.

Technical 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.