TY  - JOUR
AU  - Zorić, Martina
AU  - Cvejić, Sandra
AU  - Mladenović, Emina
AU  - Jocić, Siniša
AU  - Babić, Zdenka
AU  - Marjanović-Jeromela, Ana
AU  - Miladinović, Dragana
PY  - 2020
UR  - http://fiver.ifvcns.rs/handle/123456789/2076
AB  - 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 F-2 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 F-2 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.
PB  - Frontiers Media Sa, Lausanne
T2  - Frontiers in Plant Science
T1  - Digital Image Analysis Using FloCIA Software for Ornamental Sunflower Ray Floret Color Evaluation
VL  - 11
DO  - 10.3389/fpls.2020.584822
ER  - 

@article{
author = "Zorić, Martina and Cvejić, Sandra and Mladenović, Emina and Jocić, Siniša and Babić, Zdenka and Marjanović-Jeromela, Ana and Miladinović, Dragana",
year = "2020",
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 F-2 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 F-2 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.",
publisher = "Frontiers Media Sa, Lausanne",
journal = "Frontiers in Plant Science",
title = "Digital Image Analysis Using FloCIA Software for Ornamental Sunflower Ray Floret Color Evaluation",
volume = "11",
doi = "10.3389/fpls.2020.584822"
}

Zorić, M., Cvejić, S., Mladenović, E., Jocić, S., Babić, Z., Marjanović-Jeromela, A.,& Miladinović, D.. (2020). Digital Image Analysis Using FloCIA Software for Ornamental Sunflower Ray Floret Color Evaluation. in Frontiers in Plant Science
Frontiers Media Sa, Lausanne., 11.
https://doi.org/10.3389/fpls.2020.584822

Zorić M, Cvejić S, Mladenović E, Jocić S, Babić Z, Marjanović-Jeromela A, Miladinović D. Digital Image Analysis Using FloCIA Software for Ornamental Sunflower Ray Floret Color Evaluation. in Frontiers in Plant Science. 2020;11.
doi:10.3389/fpls.2020.584822 .

Zorić, Martina, Cvejić, Sandra, Mladenović, Emina, Jocić, Siniša, Babić, Zdenka, Marjanović-Jeromela, Ana, Miladinović, Dragana, "Digital Image Analysis Using FloCIA Software for Ornamental Sunflower Ray Floret Color Evaluation" in Frontiers in Plant Science, 11 (2020),
https://doi.org/10.3389/fpls.2020.584822 . .

TY  - CONF
AU  - Cvejić, Sandra
AU  - Zorić, Martina
AU  - Mladenović, Emina
AU  - Babić, Zdenka
AU  - Jocković, Milan
AU  - Gvozdenac, Sonja
AU  - Radanović, Aleksandra
AU  - Jocić, Siniša
AU  - Miladinović, Dragana
AU  - Marjanović-Jeromela, Ana
PY  - 2020
UR  - http://fiver.ifvcns.rs/handle/123456789/2293
AB  - Sunflower (Helianthus annuus L.) is broadly used as an ornamental plant in landscape gardening, but also as a potted plant and a cut flower. Since aesthetic traits, including color, are most important for newly developed ornamental plants, sunflower petal (ray floret) color has a high value for the development of new genotypes and its position on the horticulture market. The most common methodology for the evaluation of sunflower petals is based on UPOV guidelines for sunflower. By the guidelines, the color of sunflower ray florets can be described as yellowish white, light yellow, medium yellow, orange yellow, orange, purple, reddish brown and multicolored. Although there are photographs to define these color categories, the provided material does not give clear information on the color definition, especially the multicolored category. The main obstacle of this methodology is its high subjectivity and necessity of high expertise for evaluators. In order to make the process of evaluation of sunflower petal color more objective, we propose a new methodology that combines image segmentation (pixel-based classification), and UPOV sunflower guidelines for the definition of color groups (classes). Images of six sunflower genotypes (Ring of Fire, CMS1-30, Heliopa, Dwarf, Neoplanta and Pacino Gold) were used in the software analysis. Visual results of this process of image segmentation presented different colors for the examined varieties. This visual presentation serves as a guideline for an evaluator to determine whether there is more than one dominant color in the examined genotypes.
PB  - Goettingen : Gesellschaft fuer Pflanzenzuechtung (GPZ)
C3  - Book of Abstracts, 3rd edition, Digital breeding International Symposium of the Society for Plant Breeding e.V. (GPZ) in cooperation with Saatgut Austria, 11-13. 02. 2020., Tulln, Austria, 2020
T1  - Use of digital image analysis for the flower color evaluation in ornamental sunflower
EP  - 94
SP  - 94
UR  - https://hdl.handle.net/21.15107/rcub_fiver_2293
ER  - 

@conference{
author = "Cvejić, Sandra and Zorić, Martina and Mladenović, Emina and Babić, Zdenka and Jocković, Milan and Gvozdenac, Sonja and Radanović, Aleksandra and Jocić, Siniša and Miladinović, Dragana and Marjanović-Jeromela, Ana",
year = "2020",
abstract = "Sunflower (Helianthus annuus L.) is broadly used as an ornamental plant in landscape gardening, but also as a potted plant and a cut flower. Since aesthetic traits, including color, are most important for newly developed ornamental plants, sunflower petal (ray floret) color has a high value for the development of new genotypes and its position on the horticulture market. The most common methodology for the evaluation of sunflower petals is based on UPOV guidelines for sunflower. By the guidelines, the color of sunflower ray florets can be described as yellowish white, light yellow, medium yellow, orange yellow, orange, purple, reddish brown and multicolored. Although there are photographs to define these color categories, the provided material does not give clear information on the color definition, especially the multicolored category. The main obstacle of this methodology is its high subjectivity and necessity of high expertise for evaluators. In order to make the process of evaluation of sunflower petal color more objective, we propose a new methodology that combines image segmentation (pixel-based classification), and UPOV sunflower guidelines for the definition of color groups (classes). Images of six sunflower genotypes (Ring of Fire, CMS1-30, Heliopa, Dwarf, Neoplanta and Pacino Gold) were used in the software analysis. Visual results of this process of image segmentation presented different colors for the examined varieties. This visual presentation serves as a guideline for an evaluator to determine whether there is more than one dominant color in the examined genotypes.",
publisher = "Goettingen : Gesellschaft fuer Pflanzenzuechtung (GPZ)",
journal = "Book of Abstracts, 3rd edition, Digital breeding International Symposium of the Society for Plant Breeding e.V. (GPZ) in cooperation with Saatgut Austria, 11-13. 02. 2020., Tulln, Austria, 2020",
title = "Use of digital image analysis for the flower color evaluation in ornamental sunflower",
pages = "94-94",
url = "https://hdl.handle.net/21.15107/rcub_fiver_2293"
}

Cvejić, S., Zorić, M., Mladenović, E., Babić, Z., Jocković, M., Gvozdenac, S., Radanović, A., Jocić, S., Miladinović, D.,& Marjanović-Jeromela, A.. (2020). Use of digital image analysis for the flower color evaluation in ornamental sunflower. in Book of Abstracts, 3rd edition, Digital breeding International Symposium of the Society for Plant Breeding e.V. (GPZ) in cooperation with Saatgut Austria, 11-13. 02. 2020., Tulln, Austria, 2020
Goettingen : Gesellschaft fuer Pflanzenzuechtung (GPZ)., 94-94.
https://hdl.handle.net/21.15107/rcub_fiver_2293

Cvejić S, Zorić M, Mladenović E, Babić Z, Jocković M, Gvozdenac S, Radanović A, Jocić S, Miladinović D, Marjanović-Jeromela A. Use of digital image analysis for the flower color evaluation in ornamental sunflower. in Book of Abstracts, 3rd edition, Digital breeding International Symposium of the Society for Plant Breeding e.V. (GPZ) in cooperation with Saatgut Austria, 11-13. 02. 2020., Tulln, Austria, 2020. 2020;:94-94.
https://hdl.handle.net/21.15107/rcub_fiver_2293 .

Cvejić, Sandra, Zorić, Martina, Mladenović, Emina, Babić, Zdenka, Jocković, Milan, Gvozdenac, Sonja, Radanović, Aleksandra, Jocić, Siniša, Miladinović, Dragana, Marjanović-Jeromela, Ana, "Use of digital image analysis for the flower color evaluation in ornamental sunflower" in Book of Abstracts, 3rd edition, Digital breeding International Symposium of the Society for Plant Breeding e.V. (GPZ) in cooperation with Saatgut Austria, 11-13. 02. 2020., Tulln, Austria, 2020 (2020):94-94,
https://hdl.handle.net/21.15107/rcub_fiver_2293 .