Pregled bibliografske jedinice broj: 1038899
How does Computer vision compare to standard colorimeter in assessing the seed coat color of common bean (Phaseolus vulgaris L.)?
How does Computer vision compare to standard colorimeter in assessing the seed coat color of common bean (Phaseolus vulgaris L.)? // Journal of central European agriculture, 20 (2019), 4; 1169-1178 doi:10.5513/JCEA01/20.4.2509 (međunarodna recenzija, članak, znanstveni)
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Naslov
How does Computer vision compare to standard colorimeter in assessing the seed coat color of common bean (Phaseolus vulgaris L.)?
Autori
Varga, Filip ; Vidak, Monika ; Ivanović, Ksenija ; Lazarević, Boris ; Širić, Ivan ; Srečec, Siniša ; Šatović, Zlatko ; Carović- Stanko, Klaudija
Izvornik
Journal of central European agriculture (1332-9049) 20
(2019), 4;
1169-1178
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
colorimetry ; common bean ; Computer vision ; landrace
Sažetak
Common bean (Phaseolus vulgaris L.) exhibits a wide range of seed coat colors and this morphological trait is widely used in cultivar identification and assessment of diversity within this species. With an advancement in technology and informatics, new methods of assessing seed color are emerging in addition to traditionally used visual observation. Due to a great variety of color measuring techniques, the evaluation of the agreement between methods is needed prior to using the methods interchangeably. Seed coat color in terms of CIE L*a*b* color coordinates of 100 common bean accessions belonging to five mono- colored landraces was assessed using two methods, colorimeter and Computer vision. The percentage difference between the two methods across all samples for L* color coordinate was 5.81%, for a* color coordinate 23.32% and for b* color coordinate 44.44%. According to Bland- Altman difference plot there is a considerable lack of agreement between the two methods. However, using stepwise discriminant analysis revealed that colorimeter method correctly classified 97% of accessions into their respective landrace, while the classification success of the Computer vision was 99%.
Izvorni jezik
Engleski
Znanstvena područja
Interdisciplinarne prirodne znanosti, Poljoprivreda (agronomija), Biotehnologija
POVEZANOST RADA
Ustanove:
Visoko gospodarsko učilište, Križevci,
Agronomski fakultet, Zagreb
Profili:
Monika Vidak
(autor)
Boris Lazarević
(autor)
Ivan Širić
(autor)
Klaudija Carović-Stanko
(autor)
Siniša Srečec
(autor)
Filip Varga
(autor)
Zlatko Šatović
(autor)