Multispectral UAV-Based Assessment of Vegetation Indices in Paddy Rice at Different Growth Stages: A Case Study in Sleman, Special Region of Yogyakarta

Ilham Mubarok; Ardan Wiratmoko; Ngadisih Ngadisih; Andri Prima Nugroho; Hammam Mananda Harahap; Faiz Ridwan Pratama; Mutiara Alifia Ramadhanty; Fahmi Arsyad; Fadel Arya Pradana; Lilik Sutiarso

doi:10.20884/1.jaber.2025.6.2.20548

Ilham Mubarok Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada. Jln. Flora No. 1 Bulaksumur Yogyakarta 55281, INDONESIA
Ardan Wiratmoko Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
Ngadisih Ngadisih Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
Andri Prima Nugroho Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
Hammam Mananda Harahap Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
Faiz Ridwan Pratama Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
Mutiara Alifia Ramadhanty Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
Fahmi Arsyad Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
Fadel Arya Pradana Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
Lilik Sutiarso Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia

DOI: https://doi.org/10.20884/1.jaber.2025.6.2.20548

Abstract

Rice growth monitoring is essential for evaluating crop condition and supporting site-specific management in precision agriculture. This study assessed rice vegetation responses at different growth stages using multispectral unmanned aerial vehicle (UAV) imagery acquired over paddy fields in Tlogoadi, Sleman Regency, Special Region of Yogyakarta, Indonesia. The observed rice fields consisted of plots at planting, tillering, and booting stages within the same landscape. Six vegetation indices were analyzed, namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), Soil-Adjusted Vegetation Index (SAVI), Chlorophyll Index Red Edge (CIRE), and Renormalized Difference Vegetation Index (RDVI). The results showed clear spatial variation in vegetation response across the paddy field area. NDVI ranged from -0.204 to 0.728, NDRE from -0.137 to 0.417, GNDVI from -0.155 to 0.589, SAVI from -0.306 to 1.092, CIRE from 1.767 to 3.572, and RDVI from -0.423 to 0.246. NDVI, GNDVI, and SAVI showed a consistent increase from planting to booting stages, indicating progressive canopy development and increased vegetation vigor. NDRE and CIRE provided complementary information related to red-edge and chlorophyll-related canopy responses, although their highest responses were observed at the tillering stage. RDVI showed a less consistent pattern, suggesting that its interpretation requires caution under heterogeneous paddy field conditions. These findings indicate that multispectral UAV imagery combined with multiple vegetation indices can effectively characterize spatial variability in rice growth stages and support precision crop management in smallholder paddy field systems.

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