Abstract

AI‑enabled GIS, remote sensing, and IoT are reshaping sugarcane‑based systems by enabling precise yield prediction, real‑time stress monitoring, and process optimization. This systematic review (2016–2026) synthesizes 20 studies on AI, IoT, GIS/remote sensing and Industry 4.0 within sugarcane cultivation and sugar manufacturing. Remote‑sensing and ML approaches (Sentinel‑1/2, Landsat‑8, UAV‑LiDAR, multi‑sensor fusion) achieve high sugarcane yield/biomass prediction accuracy (R² up to 0.97, RMSE reductions up to 59%), providing decision support for planting density, irrigation and management [1-6]. IoT‑based smart irrigation on saline soils increases sugarcane growth and reduces water use and cultivation cost [7]. AI‑IoT smart‑factory and Industry 4.0 case studies show impressive cost reductions, planning time savings and resource‑use optimization, though not sugar‑specific [8-12]. A dedicated concept paper proposes productivity impact analysis from AI‑monitored GIS and IoT in 20 Maharashtra sugar factories with explicit productivity/resource‑optimization hypotheses but no empirical results yet [13]. A bibliometric study on AI in sugar production confirms rapid growth of AI‑based sensor and systems management aimed at efficiency and sustainability across the supply chain [14]. Evidence converges on strong potential productivity and sustainability gains, but there is a clear lack of end‑to‑end, quantified studies linking field‑level AI‑GIS‑IoT data to mill‑level KPIs (recovery %, throughput, energy use). Key research gaps concern integrated value‑chain productivity metrics, factory‑level AIoT deployment in sugar, sensor fusion for nutrients and stress, and longitudinal impact and adoption studies.

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