RÉSUMÉ
The relationships between the basic physical properties of seeds of selected spindle species were evaluated for the needs of seed sorting operations. Physical properties were measured in the seeds of five spindle species, and the presence of relationships between these attributes was determined in correlation and regression analyses. The average values of the evaluated parameters were determined in the following range: terminal velocity-from 9.2 to 10.3 m s-1, thickness-from 2.57 to 3.26 mm, width-from 2.87 to 3.74 mm, length-from 3.94 to 5.52 mm, angle of external friction-from 20.7° to 24.6°, mass-from 16.5 to 33.8 mg. Spindle seeds were arranged in the following ascending order based on their geometric mean diameter: winged spindle, Hamilton's spindle, large-winged spindle, broadleaf spindle and European spindle. Spindle seeds should be separated in a sieve equipped with at least two mesh screens with slotted apertures. Depending on the processed spindle species, aperture size should range from ≠ 2.7 to ≠ 3.5 mm in the top screen, and from ≠ 2.4 to ≠ 3.0 mm in the bottom screen.
RÉSUMÉ
Efforts to predict the germination ability of acorns using their shape, length, diameter and density are reported in the literature. These methods, however, are not efficient enough. As such, a visual assessment of the viability of seeds based on the appearance of cross-sections of seeds following their scarification is used. This procedure is more robust but demands significant effort from experienced employees over a short period of time. In this article an automated method of acorn scarification and assessment has been announced. This type of automation requires the specific setup of a machine vision system and application of image processing algorithms for evaluation of sections of seeds in order to predict their viability. In the stage of the analysis of pathological changes, it is important to point out image features that enable efficient classification of seeds in respect of viability. The article shows the results of the binary separation of seeds into two fractions (healthy or spoiled) using average components of regular red-green-blue and perception-based hue-saturation-value colour space. Analysis of accuracy of discrimination was performed on sections of 400 scarified acorns acquired using two various setups: machine vision camera under uncontrolled varying illumination and commodity high-resolution camera under controlled illumination. The accuracy of automatic classification has been compared with predictions completed by experienced professionals. It has been shown that both automatic and manual methods reach an accuracy level of 84%, assuming that the images of the sections are properly normalised. The achieved recognition ratio was higher when referenced to predictions provided by professionals. Results of discrimination by means of Bayes classifier have been also presented as a reference.
