Due to the lack of reliable yield monitor for sugarcane, production factors which impact and limit stalk yield within fields are not well-known. Thus, this study aims to evaluate whether canopy sensor technology is able to identify sugarcane biomass variability and whether obtaining other agronomic variable data can assist on biomass quantification. For that, forty targeted plots were allocated within two sugarcane-producing fields and data consisted on manual biometric evaluation, aboveground biomass measurement and canopy reflectance. As an ongoing experiment, only two evaluations were addressed (~0.3 and 0.5 m stalk height). On the earliest stage, canopy sensor readings were correlated to sugarcane biomass and their sensitivity to biomass variability was high. Further, data collected on the first evaluation was efficient in predicting biomass amount after 30 days. On the second, canopy sensor readings effectiveness to predict biomass was reduced. These findings suggest that crop canopy reflectance sensing is a useful approach to investigate sugarcane biomass spatial-variability within fields on early stages.

The human population is expected to reach 9 billion by 2050 and thus high yield crop varieties need to be developed. Remote sensing can estimate crop parameters non-destructively and quickly. The aim of this study was to compare and evaluate the use of a commercial RGB camera with an expensive canopy sensor in the crop development of two legumes. The RGB camera based vegetation index (NGRDI) was compared with the canopy sensor derived vegetation indices (NDVI and NDRE) for estimating legume crop growth parameters. The results indicated that the use of a simple digital camera RGB can in some cases replace spectral canopy sensors.