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All Journal ARMADA : Jurnal Penelitian Multidisiplin
Muh. Hayatullah
University of Mataram, Mataram, Indonesia
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System IoT-AI Based on Microclimate Disease Risk Index for Early Detection of Vanilla Plant Diseases Muh. Hayatullah; Raodatul Putri; Nur Safitri; Misbahuddin; Muhammad Husni Idris
ARMADA : Jurnal Penelitian Multidisiplin Vol. 4 No. 3 (2026): ARMADA : Jurnal Penelitian Multidisplin, March 2026
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/armada.v4i3.1940

Abstract

The vanilla plant (Vanilla planifolia) is a high-value commodity, but it is highly susceptible to microclimatic fluctuations and disease attacks, especially stem and root rot closely related to Fusarium oxysporum f. sp. vanillae. A review of the literature also shows that temperature, humidity, and shade conditions affect vanilla growth, whereas conventional monitoring approaches often detect disease risk too late. This paper presents a systematic literature study with the help of Google Scholar-based Publish or Perish (PoP), enriched by targeted searches on ScienceDirect and Web of Science, and reported to follow the principles of PRISMA 2020. The synthesis results show that the integration of IoT, microclimate sensors, and AI has the potential to form a more precise Early Warning System through the MDRI index, which is a weighted risk score that collects parameters of temperature, relative humidity, VPD, light intensity, soil moisture, and history of daily conditions. Conceptually, MDRI can be applied to edge devices to provide early warnings, recommendations for cultivation actions, and the basis for data-driven decision-making. This paper emphasizes that the IoT–AI approach is not just a monitoring tool, but the foundation of an adaptive and sustainable vanilla disease risk management system.
Co-Authors Misbahuddin, Misbahuddin Muhammad Husni Idris Nur Safitri Raodatul Putri