Abstract
To develop new sunflower varieties, it is important to accurately assess phenotypic traits that influence yield, disease resistance, and stress tolerance. Automation allows for systematic data collection and more informed decision-making in the breeding process. The goal of the work was to enhance the efficiency of selecting and predicting the development of sunflower genotypes through the development of new methods, hardware, and software tools for quantitative phenotypic characterisation of seeds. The methods for determining phenotypic characteristics of sunflower seeds, including geometric dimensions, mass, colour, rheological properties, and seed surface properties, have been presented and improved. A module for determining the morphological properties of seeds (geometric dimensions, mass, surface colour, etc.) was developed. This module was configured for high precision in the individual measurement of the geometric dimensions of sunflower seeds, with the determination of their shape and colour. It ensured low labour intensity and high technological efficiency in the implementation of the phenotyping procedure (determination, identification, and separation) of seeds. The methodology for analysing the rheological properties of seeds was refined, and a method for their automatic determination, along with a corresponding module, was substantiated. The proposed module maintains the accuracy of individual measurement of the rheological properties of seeds, consistent with modern measurement tools, while ensuring low labour intensity and high technological efficiency. Additionally, the module significantly reduced the influence of the human factor on the accuracy of measuring the rheological properties of seeds. The proposed module for determining seed surface properties ensured the accuracy of individual measurements of the coefficients of static and sliding friction of seeds, aligning with modern measurement tools, while also ensuring low labour intensity and high technological efficiency. This also significantly minimised the influence of the human factor on the accuracy of these measurements. The use of automated devices in practical conditions can help optimise the selection of seeds with the best characteristics