Morphological Characteristics of Grapevine Cultivars and Closed Contour Analysis with Elliptic Fourier Descriptors

Abstract

Morphology is the most visible and distinct character of plant organs and is accepted as one of the most important tools for plant biologists, plant breeders and growers. A number of methods based on plant morphology are applied to discriminate in particular close cultivars. In this study, image processing analysis was used on 20 grape cultivars (“Amasya beyazı“, “Antep karası“, “Bahçeli karası”, “Çavuş“, “Cevşen“, “Crimson“, “Dimrit“, “Erenköy beyazı“, “Hafızali“, “Karaşabi“, “Kırmızı“, “İzabella (Isabella) “, “Morşabi“, “Müşgüle“, “Nuniya“, “Royal“, “Sultani çekirdeksiz (Sultanina)“, “Yalova incisi“, “Yerli beyazv“, “Yuvarlak çekirdeksiz“) to classify them. According to image processing analysis, the longest and the greatest projected area values were observed in “Antep karası“ cultivar. The “Sultani çekirdeksiz“ cultivar had the least geometric mean diameter. The greatest sphericity ratios were observed in “Yerli beyaz“, “Erenköy beyazı“ and “Amasya beyazı“ cultivars. According to principal component analysis, dimensional attributes were identified as the most significant source of variation discriminant grape cultivars from each other. Morphological differences between the cultivars were explained by sphericity and elongation variables. According to elliptic Fourier analysis (EFA) results, grape morphology largely looks like ellipse and sphere. However, there are some cultivars that look similar to a water drop. The cultivars with similar morphology were identified by a pair-wise comparison test conducted with the use of linear discriminant analysis, and they were presented in a scatter plot. According to cluster analysis, present grape cultivars were classified into seven sub-groups, which indicated great diversity.

Identification of Aedes aegypti and Aedes albopictus eggs based on image processing and elliptic fourier analysis

“…Images data was taken with a digital zoom of 2.5 times. Furthermore, a light source was given from below as in 32 , 33 . This was done to avoid shadows from the egg and, therefore, clear individual egg shape was obtained.…”

“…Recently, Elliptic Fourier Analysis (EFA) have been widely used in extracting and describing the shapes of agriculture products [30][31][32][33] , vectors' egg 34 and body morphology 35 . Based on previously described studies, it was stated that the Ae.…”

Varietal richness of grapevine (Vitis vinifera L.) from the Majes Valley, Peru: Identification, morphological characterization, ampelographic and genetic analysis

“…Igualmente, el carácter color del haz del limbo (4ª hoja) en hoja joven (OIV 051), separa a las variedades locales con predominancia de color amarillo de aquellas con predominancia de color verde. Kupe et al (2021) también señalan que el análisis de componentes principales indica la fuente variación entre los cultivares.…”

Vine variety identification through leaf image classification: a large-scale study on the robustness of five deep learning models

“…Compared to ampelography, a biometric method avoids subjectivity of the observations, and is adequate for computational input (Costacurta et al, 1992;Alessandri et al, 1996;Soldavini et al, 2007;Zhang et al, 2010;Bodor et al, 2012) and statistical analyses can be carried out. Different biometric approaches to data analysis have been proposed such as multivariate statistical, morphometric or neural network analyses, but in general performed on a small number of varieties and seldom with uncertainty results, especially when the number of varieties is high (Boursiquot et al, 1987;Costacurta et al, 1998Costacurta et al, , 2003Mancuso, 2001;Mancuso et al, 2002;Bodor et al, 2017;Klein et al, 2017;Pereira et al, 2017;Kupe et al, 2021). However, the above grapevine identification techniques are time-consuming and require adequate equipment and specialists with a lot of expertise.…”