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3D Data Processing to Characterize the Spatial Variability of Sugarcane Fields


The adoption of precision agriculture involves a demand for equipment and solutions to create an accurate diagnostic of the spatial variability to be managed at the field level. Sugarcane has faced some challenges due to the limited solutions adapted to the crop, which develops throughout the year and involving a large-scale harvest. LiDAR (Light Detection and Ranging) technology is a high-resolution tool that permits the measurement of vegetative growth in a non-destructive way, assisting, for example, in harvest planning. The objective was to describe the three-dimensional (3D) data processing to characterize the spatial variability of sugarcane fields in the pre-harvest period. An aerial platform was used for data acquisition 10 days before and after harvesting. The digital models of surface, of terrain, and the canopy height model (CHM) were generated to spatialize plants height based on point cloud. The LiDAR-derived metrics extracted were percentiles (P50th; P90th–P99th), with the highest value of the coefficient of variation observed for the P50th (59%), indicating that there is high spatial variability in plant height. The RMSE (Root Mean Squared Error) among field measurements and sugarcane stalk height from CHM was 0.47 m. This study demonstrates that 3D sensing data can provide relevant information for the assessment of the crop height and, potentially, to consider it as an indicator of the field regions with distinct levels of production.

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To the operational support of SAI Brazil and São Manoel sugarcane mill. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.

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Correspondence to Tatiana Fernanda Canata.

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Canata, T.F., Martello, M., Maldaner, L.F. et al. 3D Data Processing to Characterize the Spatial Variability of Sugarcane Fields. Sugar Tech 24, 419–429 (2022).

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  • Precision agriculture
  • Point clouds
  • Remote sensing
  • Site-specific