Location: Sugarcane ResearchTitle: Ratoon family selection using remote sensing
Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 11/11/2020
Publication Date: N/A
Technical Abstract: Remote sensing techniques and the use of Unmanned Aerial Systems (UAS) have simplified the estimation of yield and plant health in many crops. Family selection in sugarcane breeding programs relies on weighed plots at harvest, which is a labor intensive process. In this study we utilized UAS-based remote sensing to estimate family yields for a second ratoon crop. Multiple families from the commercial breeding program were planted in a randomized complete block design by family. Standard red, green, and blue imagery was acquired with a commercially available UAS and RGB camera. The CIMMYT Breedpix software was utilized to calculate color indices using the CIELab color space model. Data mining software and multiple linear regression were used to identify the best model to estimate second ratoon cane yield (kg/Mg). A multiple regression model which included family, and five different color indices produced a significant adjusted R2 of 0.66. This indicates that it is possible to make family selection predictions of cane weight without harvesting the field. The adoption of this technology has the potential decrease labor requirements and increase breeding efficiency.