<![CDATA[Block-level spatial patterning of oil palm is often under-measured for actionable precision management. This study analyzes the spatial pattern of oil palm trees in Blocks N39 and P39 at PT Nusantara Sawit Persada. High-resolution UAV imagery was processed into orthophotos, crowns were automatically detected in eCognition Developer using template matching, and spatial patterns were evaluated with Nearest Neighbor Analysis. The distribution was significantly dispersed (Nearest Neighbor Ratio > 1; Z-score > 97). Morphometric and age variables jointly explained productivity (R² > 0.80). An integrated workflow combining UAV imagery, eCognition object-based analysis, and Average Nearest Neighbor (ANN) delivers accurate plantation diagnostics and supports data-driven precision management, including block-level planning, targeted fertilization, timely replanting, and harvest scheduling. Keywords: Spatial Patterns, Oil Palm Trees, UAV]]>
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