Location: Sugarcane ResearchTitle: Family selection with vegetation indices derived from UAS acquired aerial imagery
Submitted to: American Society of Sugar Cane Technologists
Publication Type: Abstract Only
Publication Acceptance Date: 4/2/2018
Publication Date: 9/1/2018
Citation: Todd, J.R., Johnson, R.M. 2018. Family selection with vegetation indices derived from UAS acquired aerial imagery [abstract]. Journal of the American Society of Sugar Cane Technologists. 38:66.
Technical Abstract: Remote sensing techniques can successfully estimate plant health and yield for many crops. In addition, the widespread use of Unmanned Aerial Systems (UAS) has simplified the imagery acquisition process. Currently, family selection in sugarcane breeding programs is a labor intensive process involving harvesting entire field plots of plant cane. In this study we utilized UAS-based remote sensing to estimate family yields in the first ratoon crop before field selection. Multiple families from the commercial breeding program were planted in a randomized complete block design by family in 2016. Standard red, green, and blue imagery was acquired with a commercially available UAS and camera. The images were converted to the visible to the atmospherically resistant index (VARI), the triangular vegetation index (TVI) and the normalized green red difference index (NGRDI) and an average pixel count was measured for each plot. All plots were then mechanically harvested and weighed. The first ratoon yield data were then correlated with the derived indices. The correlation coefficients between the averages of the VARI, TVI and NGRDI indices and weighed plots were 0.59 (p < 0.01), 0.15 (p =0.52), and 0.55 (p =0.01) respectively. The results from this experiment indicate that the VARI and NGRDI indices may be useful in identifying family yield differences in seedling sugarcane plots. Adoption of these techniques may significantly reduce future labor requirements, while maintaining or increasing the selection efficiency of sugarcane breeding programs.