Fruit Shape Analysis of Vitis Using Digital Photography

Abstract

<jats:p>Quantifying fruit shape is challenging, particularly when measurements are made on segregating populations of plants. Objective manual measurements can be performed on small samples of fruit, but this method is difficult and very time-consuming when dealing with larger samples or when shapes are complex or shape variations are slight. Subjective rating scales can also be used, but their effectiveness is questionable when done by multiple raters resulting from varying descriptive standards among individuals. Therefore, a method was developed to analyze digital images containing multiple fruits to characterize fruit shapes. Each segregant of a population of table grapes (<jats:italic>Vitis</jats:italic> spp.) with parents of wide shape variation was photographed and analyzed for shape using SigmaScan<jats:sup>®</jats:sup> software. The program discriminately selected image pixels representing the fruit and determined the area and perimeter of a grape berry, which were subsequently used to calculate the major:minor axis ratio, shape factor, and compactness values. Computer findings were compared with data from human raters using a simple correlation. When compared with the human ratings, results showed strong correlations of r = 0.941 for major:minor axis ratio, r = –0.804 for shape factor, and r = 0.744 for compactness. This analysis method was a reasonably quick and simple way to quantify grape berry shape, yielding valuable phenotypic data in numerical form. This technology should be useful for shape characterizations in other fruits as well.</jats:p>

A Low-Cost and Unsupervised Image Recognition Methodology for Yield Estimation in a Vineyard

“…The literature reports several attempts, with the use of computer vision, to characterize morphological attributes of grapes (Wycislo et al, 2008; Miao et al, 2012), to evaluate different cluster and berry traits (Kicherer et al, 2013; Cubero et al, 2014; Diago et al, 2014; Roscher et al, 2014; Tello and Ibanez, 2014; Aquino et al, 2017) and to assess the number of grapevine flowers per inflorescence (Diago et al, 2014; Aquino et al, 2015).…”

A new method for assessment of bunch compactness using automated image analysis

“…Recently, Herzog et al (2014) have shown some initial results on the application of image analysis for high-throughput phenotyping in vineyards. Wycislo et al (2008) used different ratios, such as the major/minor axis ratio, shape factor, and compactness shape value, to estimate the shape of table grapes. Wycislo et al (2008) used different ratios, such as the major/minor axis ratio, shape factor, and compactness shape value, to estimate the shape of table grapes.…”

“…Several attempts have been made for the determination of bunch morphology using image analysis. Wycislo et al (2008) used different ratios, such as the major/minor axis ratio, shape factor, and compactness shape value, to estimate the shape of table grapes. Chen et al (2010) configured an automated inspection system for grading grape bunches on the basis of their colour, size and shape.…”

Assessment of cluster yield components by image analysis

“…13 Recently, Diago et al 14 Several studies have been conducted for cluster morphological determinations using image analysis. Wycislo et al 15 used different ratios to estimate the shape of tablegrapes such as the major/minor axis ratio, shape factor, and compactness shape value. Recently, a new method for cluster peduncle detection was developed using image analysis.…”