Biometric characteristics and canopy reflectance association for early-stage sugarcane biomass prediction

Abstract

Knowing the spatial variability of sugarcane biomass in the early stages of development may help growers in their management decision-making. Proximal canopy sensing is a promising technology that can identify this variability but is limited to quantifying plant-specific parameters. In this study, we evaluated whether biometric variables integrated with canopy reflectance data can assist in the generation of models for early-stage sugarcane biomass prediction. To substantiate this assertion, four sugarcane-producing fields were measured with an active crop canopy sensor and 30 sampling plots were selected for manually quantifying chlorophyll content, plant height, stalk number and aboveground biomass. We determined that Random Forest and Multiple Linear Regression models are similarly able to predict biomass, and that associating biometric variables such as number of stalks and plant height with reflectance data can assist model performance, depending on the attributes selected. This indicates that, when estimating biomass in the early stages, sugarcane growers can carry out site-specific management in order to increase yield and reduce the use of inputs.

High-Resolution Imagery Data to Assess the Spatial Variability of Sugarcane Fields/ Dados De Imagens De Alta Resolução Para Avaliação Da Variabilidade Espacial De Talhões De Cana-De-Açúcar

“…The VI showed the best correlations with yield predominantly in the period of growth of sugarcane stalks in all the study scenarios and fields. Amaral et al (2015b) obtained reasonable correlation (r = 0.81) for both NDVI and NDRE from canopy sensors and biomass when the sugarcane stalk height was 0.5 m. Rocha et al (2019) identified that NDRE associated with the number and height of the sugarcane stalks, can assist in the prediction of biomass in the early stages of the crop.…”