{'fraction_non_alphanumeric': 0.022052927024859663, 'fraction_numerical': 0.008821170809943865, 'mean_word_length': 5.2375, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
The Fractal Dimension of Grapevine Leaves as a Tool for Ampelographic Research

Morphological leaf characters and quantitative measurements of anatomical elements of the leaf, i.e. angles, area, teeth number, petiole length, have been extensively utilised in ampelographic research (OIV-IBPGR-UPOV charts 1983; Galet 1985). However, the origin of the grapevine varieties, their heterogeneity and the frequent cases of homonimy and synonymy, often resulted in doubtful classification. It is thus important to define good shape measures that can be effectively applied to leaf shapes, so they can be compared and analysed by meaningful and objective criteria. One approach that researchers have proposed for describing biological shapes is the fractal based measure of digitally acquired images. Many pattern of nature are either irregular or fragmented to such an extreme degree that Euclidean geometry cannot describe their form. Thus, fractal geometry based analysis has received increasing attention as a number of studies have shown fractal based measures to be useful for characterising complex biological structures. Fractal scaling is evident in natural objects from the microscale to the macro-scale, e.g. the human body contains many structures with fractal characteristics. In fact, it has been found that non-fractal objects were the exception, rather than the rule in many natural systems. Thus, it seemed interesting to verify the possible application of fractal analysis to describe grapevine leaves (Figure 1) belonging to different genotypes with the hope to add an objective, clarifying dimension to the excessively convoluted field of ampelography. The study was carried out with 11 putative Sangiovese-related ecotypes and the registered clone Sangiovese R 10 as a reference (Table 1). Samples were collected from the grapevine germplasm collection of the Department of Horticulture of the University of Florence, Italy. At the veraison, 50 fully expanded, healthy leaves, from 15 plants per accession, located between the 7 and 11 shoot node from the apex were selected according to uniformity of appearance, growth habit and exposure. The steps of the box-counting algorithm were as following. The original grayscale image was thresholded to create a binary image, where leaves were represented by black pixels. An edge detection algorithm was applied to the binary image to create an image containing only the edge of the leaf. Figure 1: An image of grapevine leaf analysed