For the proper development of the potato (Solanum tuberosum L.), it is essential to provide the plant with micronutrients, including selenium. The availability of the potato for starch industry after harvest is determined by proper storage conditions. Research was undertaken to determine tuber yield, starch yield and quality characteristics of potato starch in relation to cultivar and foliar feeding of plants with selenium during cultivation. Two-factor field experiment was conducted in 2018–2019: I factor – cultivar (Albatros, Jumbo, Maxilla Terrana, Transit), II factor – selenium fertilization (0, 10, 20 g Se/ha). The tests were carried out directly after harvest and after 6 months of tubers storage under constant conditions: temperature 4°C, 95% RH. The highest total tuber and starch yields were obtained by potatoes after application of 20 g Se/ha: 39.42 and 7.94 Mg/ha, respectively. Proportion of large starch grains (>20 μm) was significantly higher for cv. Maxilla – 24.5%. The highest sticking temperature was demonstrated by starch from cv. Maxilla, 69.9°C, while the lowest for the starch of cv. Terrana, 63.3°C. The largest natural losses resulting from transpiration and respiration after long-term storage of tubers received the cv. Transit – 5.0%, and the smallest losses were characterized by the cv. Albatros – 3.9%. Selenium applied during cultivation at dose of 20 g/ha reduced natural losses (−4.3%) and yield starch (−6.2%) and starch content (−1.7%) in tubers. The quality parameters of starch were changed after storage from 0.8 to −3.0%.

The “sketch” drawn by a human designer represents a shape class of wider variability than can be captured by traditional CAD models; these typically work with parametrizations based on a nearly finished shape. Traditional Qualitative Reasoning is also unable to model this degree of ambiguity in shape. Cognitively, shapes are often represented in terms of an axial model. In defining 2D contours, such an axial representation is called the Medial Axis Transform or MAT. By perturbing the parameters of the MAT—length, link angle, and the node radius—one can define a shape class. Unlike the contour-to-MAT transform, which is well-known to be unstable, the MAT-to-contour process is an integrative process and is very stable. The variation in these parameters can be controlled by defining a suitable discretization over the parameter space. This leads to a broad class of similar shapes from which an optimized shape can be obtained for a given set of criteria. The optimizing criteria may involve the boundary description for each shape; the axial model is only used for generating the shape class. This Qualitative MAT model has been tested in several design optimization contexts, using Genetic Algorithms, and we show results for Automobile contours, IC engine parts, building profiles, etc.