Early Detection of Red Palm Weevil Infestation: A Systematic Review of Sensing Technologies, Artificial Intelligence, and Field Readiness

The red palm weevil (Rhynchophorus ferrugineus Olivier; RPW) is an economically important invasive pest of palms, and early detection remains challenging because larval infestation develops internally before visible symptoms appear. This systematic review evaluates current RPW detection technologies, their operational limitations, and priorities for future development. Following PRISMA 2020, 56 primary studies were included, principally covering the period from 2015 to July 2026, together with selected seminal earlier studies. The evidence base was established through a systematic search across five bibliographic and scholarly sources, complemented by a structured supplementary update and source-verification procedure covering publications through 25 July 2026. Acoustic and vibroacoustic sensing provided the most direct evidence of active internal larval activity, whereas thermal, spectral, RGB, and UAV-based approaches primarily detected host physiological stress or externally observable damage. Although several studies reported high classification performance, most evidence was derived from private, single-site, proxy, or controlled datasets, with limited external field validation and inconsistent evaluation protocols. Multimodal and IoT-enabled systems showed potential for continuous and scalable monitoring, but consistent superiority over matched unimodal baselines was not established. No included study explicitly implemented and quantitatively evaluated a recognised explainable artificial intelligence method. The review also identifies major benchmarking and reproducibility limitations and proposes a conceptual six-layer weather-aware multimodal AI framework to guide the development of environmentally robust and field-deployable RPW monitoring systems.