Prediction of Ratoon Sugarcane Family Yield and Selection Using Remote Imagery

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.

UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane

“…Therefore, implementing a flawless filter could be necessary to increase its accuracy and precision in processing data with significant fluctuations. Even though MLR is (Todd and Johnson, 2021;Krupavathi et al, 2022). However, it could not predict °Brix as accurately as RF and SVM, supporting a non-linear dataset.…”

“…Even though MLR is topologically and operationally more basic than other ML algorithms, it can develop a highly accurate predictive model for Purity. Such a technique can work well on linear imagery data; hence, it can offer a reliable estimate of quantitative variables, such as productivity, upon VIs ( Todd and Johnson, 2021 ; Krupavathi et al., 2022 ). However, it could not predict °Brix as accurately as RF and SVM, supporting a non-linear dataset.…”

Yield component evaluation of mutated sugarcane at first and ratooning plantations

“…The KK value of more than 75% obtained by all mutant sugarcane genotypes indicates the stable sucrose content of mutant sugarcane until the second ratoon. Selection of the best genotypes based on sucrose content will improve selection efficiency and increase heritability (Todd & Johnson, 2021). The high ratooning ability of each sugarcane genotype indicated its characteristics as a superior variety.…”