Laravel Dashboard for Immature Oil Palm (TBM III) Monitoring Using XYZ Tiles and Large Language Models - Maghfirah; Ritna Wahyuni; Raden Aris Sugianto (Journal of Deep Learning, Computer Vision, and Digital Image Processing, 2026)
Purpose – This research addresses the strategic urgency of digitalizing plantation monitoring to achieve precision agriculture at PTPN IV Regional I. The monitoring of Immature Plants (TBM) III currently relies on fragmented manual spreadsheets, leading to data redundancy and delayed analysis.Methods – A web-based data visualization dashboard was developed using the Laravel framework, integrating Geographic Information Systems (GIS) with Static Raster Tiling (XYZ Tiles) to optimize high-resolution map rendering. The system incorporates Large Language Model (LLM) API integration (Gemini 1.5 Flash and Llama 3) for prescriptive analytics, transforming biometric growth data into automated maintenance recommendations through prompt engineering.Findings – Results indicate that the system achieves significant workflow simplification by transforming the fragmented, multi-stage manual reporting pipeline into an automated, single-step data ingestion process, successfully reducing administrative touchpoints. The Static Raster Tiling (XYZ Tiles) technique successfully reduced high-resolution orthophoto rendering latency from over 12,000 ms to an average of 180 ms. Validation using Fleiss' Kappa statistics yielded a score of 0.8105, categorized as "Almost Perfect Agreement," confirming that the AI-generated recommendations are highly consistent with expert agronomic standards. Research implications – This system provides a comprehensive managerial evaluation tool, bridging the gap between raw field data and strategic decision-making in oil palm management.Originality – The integration of spatial optimization and prescriptive AI analytics offers a novel approach compared to existing descriptive-only monitoring platforms.