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- Author or Editor: Andrew P. Wycislo x
Quantifying fruit shape is challenging, particularly when measurements are made on segregating populations of plants that vary greatly in shape. Objective manual measurements can be performed on small samples of fruit, but this method is not feasible when dealing with larger samples or when shape variations are slight and continuous. Also, subjective rating scales can be utilized, but they are less effective when done by multiple raters due to varying descriptive standards among individuals. Therefore, we have developed a method to analyze digital images containing multiple fruits to characterize fruit shapes. Each segregant of a population of table grapes with parents of significant varying shapes was photographed and analyzed. Image pixels representing fruit were selected and evaluated for area and perimeter, which were subsequently used to calculate a shape factor and compactness value. This was a reasonably simple and quick method for quantifying grape berry shape, giving the researcher valuable phenotypic data in numerical form. This technology should be useful for shape characterizations of other fruits as well.
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 (Vitis spp.) with parents of wide shape variation was photographed and analyzed for shape using SigmaScan® 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.