The Potential Application of IoT and Multispectral UAV Soil Sensor Technology in Sorgum (Sorgum bicolor L. Moench) Cultivation in Dry Land in Pujut District, Central Lombok Regency
DOI:
10.29303/jbt.v26i2.11867Published:
2026-04-19Downloads
Abstract
Sorghum (Sorghum bicolor L. Moench) is a drought-tolerant crop with significant potential for dryland cultivation in Pujut District, Central Lombok Regency, Indonesia. This study reviews the potential application of Internet of Things (IoT) and multispectral Unmanned Aerial Vehicle (UAV) technologies for precision sorghum cultivation in dryland ecosystems. A qualitative descriptive literature review was conducted, synthesizing 13 peer-reviewed studies on IoT sensor networks, UAV-based remote sensing, machine learning algorithms, and their integration in precision agriculture. The results indicate that IoT soil sensors can continuously monitor soil moisture, temperature, pH, and nutrient levels in Vertisol soils, while multispectral UAVs capture vegetation indices (NDVI, NDRE, CWSI) for biomass estimation, drought stress assessment, and yield prediction. The integration of both technologies, combined with machine learning approaches including ensemble learning and transfer learning, produces comprehensive crop health maps and site-specific management recommendations. The dryland characteristics of Pujut District, with Vertisol soils (pH 6.5–8.4) and limited water availability, are highly suitable for sorghum cultivation and would benefit substantially from precision agriculture interventions. A five-stage implementation framework is proposed, encompassing baseline survey, monitoring, analytics, precision management, and evaluation. Despite challenges including initial investment costs and technical capacity requirements, the long-term benefits of improved productivity and resource efficiency make IoT-UAV integration a viable strategy for sustainable dryland sorghum farming.
Keywords:
Dryland farming IoT Machine learning NDVI UAV multispectral Sorghum Precision agricultureReferences
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