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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 imagery of plant-cane and first ratoon crops 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 equipped with a Red–Green–Blue (RGB) camera. Color indices using the CIELab color space model were estimated from the imagery for each plot. The cane was mechanically harvested with a sugarcane combine harvester and plot weights were obtained (kg) with a field wagon equipped with load cells. Stepwise regression, correlations, and variance inflation factors were used to identify the best multiple linear regression model to estimate the second ratoon cane yield (kg). A multiple regression model, which included family, and five different color indices produced a significant R2 of 0.88. This indicates that it is possible to make family selection predictions of cane weight without collecting plot weights. The adoption of this technology has the potential to decrease labor requirements and increase breeding efficiency.
Highlights
Sugarcane is an economically important crop in Louisiana [1]
The objective of this study was to (1) determine if remote sensing imagery acquired by Unmanned Aerial Systems (UAS) could accurately and efficiently evaluate the seedling family performance of cane yield in Houma and (2) determine if second ratoon cane yield could be estimated from plant cane and first ratoon images
Family selection based on plant cane weighed plot data is not accurate for selecting second ratoon family cane yield
Summary
Sugarcane is an economically important crop in Louisiana [1]. To increase profitability, sugarcane varieties are constantly being improved by the United States Department of Agriculture–Agricultural Research Service’s (USDA–ARS) Louisiana sugarcane variety development program, located in Houma, LA. An important part of this program is the selection of sugarcane seedlings, which requires the evaluation of 70–80 thousand seedlings over a short period of time by brief visual inspection, which requires significant time and labor. As these seedlings are individually un-replicated, selection is potentially biased due to spatial arrangement and microenvironments. Selection, which involves the selection of seedling families instead of individuals, is based on data from replicated family plots This procedure is more efficient because fewer poor performing individuals with low heritability traits are introduced into the program [2], but family selection requires gathering data such as weighed plot yields from seedling plots. Selection in sugarcane at the seedling stage is widely practiced around the world in places, such as Australia [3,4,5,6,7] and the United States [8,9,10,11]
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