Digital Image Analysis Using FloCIA Software for Ornamental Sunflower Ray Floret Color Evaluation

Martina Zorić; Sandra Cvejić; Emina Mladenović; Siniša Jocić; Zdenka Babić; Ana Marjanović Jeromela; Dragana Miladinović

doi:10.3389/fpls.2020.584822

Digital Image Analysis Using FloCIA Software for Ornamental Sunflower Ray Floret Color Evaluation

Front Plant Sci. 2020 Nov 9:11:584822. doi: 10.3389/fpls.2020.584822. eCollection 2020.

Authors

Martina Zorić¹, Sandra Cvejić², Emina Mladenović³, Siniša Jocić², Zdenka Babić⁴, Ana Marjanović Jeromela², Dragana Miladinović²

Affiliations

¹ Institute of Lowland Forestry and Environment, University of Novi Sad, Novi Sad, Serbia.
² Sunflower Department, Institute of Field and Vegetable Crops, Novi Sad, Serbia.
³ Faculty of Agriculture, University of Novi Sad, Novi Sad, Serbia.
⁴ Faculty of Electrical Engineering, University of Banja Luka, Banja Luka, Bosnia and Herzegovina.

Abstract

As an esthetic trait, ray floret color has a high importance in the development of new sunflower genotypes and their market value. Standard methodology for the evaluation of sunflower ray florets is based on International Union for the Protection of New Varieties of Plants (UPOV) guidelines for sunflower. The major deficiency of this methodology is the necessity of high expertise from evaluators and its high subjectivity. To test the hypothesis that humans cannot distinguish colors equally, six commercial sunflower genotypes were evaluated by 100 agriculture experts, using UPOV guidelines. Moreover, the paper proposes a new methodology for sunflower ray floret color classification - digital UPOV (dUPOV), that relies on software image analysis but still leaves the final decision to the evaluator. For this purpose, we created a new Flower Color Image Analysis (FloCIA) software for sunflower ray floret digital image segmentation and automatic classification into one of the categories given by the UPOV guidelines. To assess the benefits and relevance of this method, accuracy of the newly developed software was studied by comparing 153 digital photographs of F₂ genotypes with expert evaluator answers which were used as the ground truth. The FloCIA enabled visualizations of segmentation of ray floret images of sunflower genotypes used in the study, as well as two dominant color clusters, percentages of pixels belonging to each UPOV color category with graphical representation in the CIE (International Commission on Illumination) L^∗a^∗b^∗ (or simply Lab) color space in relation to the mean vectors of the UPOV category. Precision (repeatability) of ray flower color determination was greater between dUPOV based expert color evaluation and software evaluation than between two UPOV based evaluations performed by the same expert. The accuracy of FloCIA software used for unsupervised (automatic) classification was 91.50% on the image dataset containing 153 photographs of F₂ genotypes. In this case, the software and the experts had classified 140 out of 153 of images in the same color categories. This visual presentation can serve as a guideline for evaluators to determine the dominant color and to conclude if more than one significant color exists in the examined genotype.

Keywords: breeding; classification; digital UPOV; ornamental value; software; sunflower.