A statistical approach to the morphological classification of Prunus sp. seeds

Abstract

The use of digital image analysis for discriminating between and comparing groups of seeds is becoming an increasingly common practice in taxonomic studies. For this type of study, many variables, concerning different kinds of data such as size, texture and shape, are generally used as inputs in statistical algorithms without any data pre-processing, thereby generating problems with noise and the consistency of the process for new samples. We propose an approach in which the variables for each kind of data are separately pre-processed by performing principal component analysis and Fourier analysis. Furthermore, the accuracy of the different kinds of data is measured by comparing the results obtained using several classification algorithms: k-Nearest Neighbour, Linear Discriminant Analysis, Naive Bayes, Support Vector Machines and Random Forest. We have taken as a case study the seeds of 19 cultivars of Sardinian Prunus domestica L. and four cultivars referable to other Prunus species. The combination of size, texture and shape data was able to perform well in discriminating between the seeds of Prunus sp. The present study confirms that image analysis techniques combined with the pre-processing of data are a useful tool for taxonomic investigation in plant biology and for discrimination at the cultivar level.

Keywords:

• Digital image analysis
• Fourier analysis
• LDA
• Prunus sp.
• seeds
• stream data

Acknowledgements

The research activities of Luca Frigau described in this paper have been conducted within the R&D project “Cagliari2020” partially funded by the Italian University and Research Ministry (grant No. MIUR_PON04a2_00381). The research activities of Luca Frigau and Francesco Mola have been partially supported by the Regione Autonoma della Sardegna under the Grant Pacchetti Integrati di Agevolazione Industria, Artigianato e Servizi, PIA – 2013 No. 282/13 and by the Italian University and Research Ministry (Progetto Dipartimenti di Eccellenza 2018–2022). The work of J. Antoch has been partially supported by grant GACR P403/19/02773S.

Disclosure statement

No potential conflict of interest was reported by the authors.