High-resolution banana row maps for the characterization of spatial variability in the field

Abstract

ABSTRACT Precision agriculture (PA) practices in banana production chains have received limited attention. Based on the literature, the investigation of spatial and temporal variability in banana orchards should be customized according to the characteristics of the crop. This study aimed to develop and evaluate methods for mapping the spatial variability in soil properties at row- and clump-resolutions in a banana orchard, and to generate row and clump maps with high-spatial-resolution soil property information. A banana orchard was investigated, and georeferenced soil sampling was conducted with calibration and validation points. Methods for reconstructing banana rows and clumps were proposed, called Methods 1 and 2 and Alternative Methods 1 and 2. Surface and line maps at row- and clump-resolutions for soil chemical and physical properties were generated using ordinary kriging and Voronoi polygons. Subsequently, the discrepancies between the data obtained from the validation points and the predictions devised from the surfaces generated by the proposed approaches were calculated, and the RMSE was used as a performance parameter. Methods 1 and 2 were appropriate and reliable approaches for site-specific management and allow for specific and optimized crop management in banana cultivation, offering greater accuracy in cultivation operations such as fertilization.