Open AccessTaxonomic determination and physiological analysis of ornamental plants using convolutional neural networks
Author(s) -
Владислав Юрьевич Бондаренко,
А. В. Барковский,
Антонина Юрьевна Шашко,
Мария Александровна Черныш,
Дарья Андреевна Пржевальская,
Дмитрий Викторович Колбанов,
Анатолий Иосифович Соколик,
Игорь Иванович Смолич,
С.С. Медведев,
Vadim Demidchik
Publication year - 2019
Publication title -
žurnal belorusskogo gosudarstvennogo universiteta. bilologiâ/žurnal belorusskogo gosudarstvennogo universiteta. biologiâ
Language(s) - English
Resource type - Journals
eISSN - 2617-3964
pISSN - 2521-1722
DOI - 10.33581/2521-1722-2019-1-25-32
Subject(s) - phenomics , artificial intelligence , convolutional neural network , computer science , artificial neural network , pattern recognition (psychology) , machine learning , deep learning , rgb color model , biology , genomics , biochemistry , genome , gene
Phenomics is an interdisciplinary scientific field, the object of research of which are phenotypes, their qualitative and quantitative parameters, as well as regularities of their formation during ontogenesis and as a result of interaction with external factors. The methodology of phenomics is a set of approaches for the phenotyping of plants, including a number of the most modern technologies of imaging, spectral analysis, biochemical, molecular and genetic analyses, and also innovative informatics techniques such as image recognition, computer vision and machine learning. The purpose of this work was to develop a phenomics application based on computer vision and methods of machine learning for taxonomic classification and determination of physiological condition of different ornamental plants. As a result of this work, the annotated databases Thuja occidentalis L., Forsythia intermedia Vahl, Heuchera micrantha Douglas ex Lindl., Syringa vulgaris L., Phalaenopsis × hybridum Blume, etc. were created and annotated. The model of a convolution neural network for taxonomic classification and determination of physiological condition of plants on the basis of RGB-images was developed. The training used images obtained in standardized conditions by high quality RGB-cameras. The neural network showed high efficiency of recognition, when analysing with taxonomic properties of decorative plants (about 90.8 %). The developed neural network also demonstrated coefficient of determination (R2 ) about 0.66 in the analysis of physiological state.