A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture

Precision agriculture focuses on the development of site-specific harvest considering variability each crop area. Vegetation indices allow study and delineation different characteristics field zone, generally invisible to naked-eye. This paper introduces a new big data triclustering approach based evolutionary algorithms. The algorithm shows its capability discover three-dimensional patterns basis vegetation from vine crops. Different have been tested find in results reported using vineyard located Portugal depicts four areas with moisture stress particularities that can lead changes management vineyard. Furthermore, scalability studies performed, showing proposed is suitable for dealing datasets.