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The red seaweed Kappaphycus alvarezii is an economically important gelling agent κappa carrageenan source. Phytochemical analysis has pointed to the presence of various other inorganic and organic compounds, which are expanding the application of biomass as a biostimulant in the agroindustry and as a source of new bioactive molecules in the food, chemical, and pharmaceutical industries. Native to Southeast Asia, K. alvarezii has been introduced as an exotic species in Brazil for commercial large-scale farming. Nowadays, legal farming areas are located in the South and on the South-East coast, but with initiatives to be authorized in the country's Northeast. The biomass yield in a large-scale farming system can be affected by cultivation techniques and environmental stressors, such as temperature, salinity, water quality, disease, and predators. The use of high-resolution images obtained with unmanned aerial vehicles (UAV or drones) is becoming a popular technology in agriculture, and it has the potential to be employed in seaweed farming to extract a variety of variables and features to predict biomass yield throughout the cultivation period. The present study was conducted to analyze and select multispectral indices obtained from images collected by drone for the detection and quantification of K. alvarezii in a commercial cultivation environment in Brazil. Frequency analysis of pixel values, statistical analyses, and visual interpretations for 24 pre-selected indices was applied according to scores attributed to the efficiency of image segmentation. This analysis resulted in the selection of four indices (ABDI1, ABDI2, CIG, and GNDVI) as the best ones for the segmentation of images in the K. alvarezii commercial farms analyzed. The data obtained are the first step in improving the analysis process of images generated by drones, which will facilitate decision-making and better management, and help scale-up K. alvarezii farming in Brazil. [ABSTRACT FROM AUTHOR]