A Comparative Analysis of RG-NIR and Multispectral Camera Imagery Acquired via Unmanned Aerial Vehicles for Sugarcane Crop Detection

Karen Slamet Hardjo, Like Indrawati, Eka Tarwaca Susila

Abstract


This study compares of two types of multispectral cameras, DJI Mavic 3M and MAPIR RGN, in assessing sugarcane health through reflectance analysis and vegetation indices. The research was conducted in a sugarcane plantation in Sidoarjo, East Java, using multispectral data captured by drones. The analysis evaluated the relationship between reflectance values, vegetation indices, and chlorophyll content in sugarcane. Results indicate that the MAPIR RGN camera outperformed the DJI Mavic 3M in measuring chlorophyll content. The Near Infrared (NIR) channel of MAPIR RGN showed the highest correlation with chlorophyll (r = 0.2166). Additionally, the Ratio Vegetation Index (RVI) from MAPIR RGN had the strongest correlation (r = 0.2716) among all vegetation indices. Conversely, the DJI Mavic 3M camera demonstrated weaker correlations across all reflectance channels and vegetation indices. These differences may stem from sensor sensitivity and the quality of data produced by each camera. Based on these findings, the MAPIR RGN camera is recommended for precision agriculture applications in sugarcane plantations, as it provides more accurate spectral data reflecting vegetation health. This study underscores the relevance of drone technology in enhancing the efficiency of sugarcane plantation management.

 

Keywords: Remote Sensing; Multispectral Camera; Drone/UAV; Precision Farming; Vegetation Index

 

DOI: http://dx.doi.org/10.23960/jpg.v13.i1.32315

 


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