This study deals with the application of a machine learning algorithm (a classification tree) to assess the weight of Corallium rubrum (Cnidaria, Octocorallia) ramifications on the basis of the number of apices. Our approach can be easily applied to obtain in situ estimates of weight and basal diameter of colonies. Future developments include the integration with image acquisition and processing hardware.
Dataset
Linares, Cristina; Figuerola, Laura; Gómez-Gras, Daniel; Pagès-Escolà, Marta; Olvera, Àngela, Aubach, Àlex; Amate, Roger; Figuerola, Blanca; Kersting, Diego; Ledoux, Jean-Baptiste; López-Sanz, Àngel; López-Sendino, Paula; Medrano, Alba; Garrabou, Joaquim; (2020); CorMedNet- Distribution and demographic data of habitat-forming invertebrate species from Mediterranean coralligenous assemblages between 1882 and 2019, meer
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