Camellia japonica L. genotypes identified by an artificial neural network based on phyllometric and fractal parameters

The potential application of phyllometric and fractal parameters for the objective quantitative description of leaf morphology, combined with the use of Back Propagation Neural Network (BPNN) for data modelling, was evaluated to characterize and identify 25 Camellia japonica L. accessions from an Italian historical collection. Results show that the construction of a BPNN based on phyllometric and fractal analysis could be effectively and successfully used to discriminate Camellia japonica genotypes using simple dedicated instruments, such as a personal computer and an easily available optical scanner.