Effect of different combinations of size and shape parameters in the percentage error of classification of structural elements in vegetal tissue of the pumpkin Cucurbita pepo L. using probabilistic neural networks

The optimal combination of size and shape parameters for classifying structural elements with the lowest percentage error is determined. For this purpose, logical sequences and a series of micrographs of tissues of the pumpkin Cucurbita pepo L. were used to identify and manually classify structural elements into three different classes: cells, intercellular spaces and unrecognizable elements. From each element, eight parameters of size and shape (area, equivalent diameter, major axis length, minor axis length, perimeter, roundness, elongation and compaction) were determined, and a logical sequence was developed to determine the combination of parameters that generated the lowest error in the classification of the microstructural elements by comparison with manual classification. It was found by this process that the minimum error rate was 12.7%, using the parameters of major axis, minor axis, perimeter and roundness.