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This study explores the application of computer vision for enhancing the selection of rootstock-graft combinations and detecting diseases in grape seedlings. Computer vision has various applications in viticulture, but publications and research have not reported the use of computer vision in rootstock-graft selection, which defines the novelty of this research. This paper presents elements of the technology for applying computer vision to rootstock-graft combinations and includes an analysis of grape seedling cuttings. This analysis allows for a more accurate determination of the compatibility between rootstock and graft, as well as the detection of potential seedling diseases. The utilization of computer vision to automate the grafting process of grape cuttings offers significant benefits in terms of increased efficiency, improved quality, and reduced costs. This technology can replace manual labor and ensure economic efficiency and reliability, among other advantages. It also facilitates monitoring the development of seedlings to determine the appropriate planting time. Image processing algorithms play a vital role in automatically determining seedling characteristics such as trunk diameter and the presence of any damage. Furthermore, computer vision can aid in the identification of diseases and defects in seedlings, which is crucial for assessing their overall quality. The automation of these processes offers several advantages, including increased efficiency, improved quality, and reduced costs through the reduction of manual labor and waste. To fulfill these objectives, a unique robotic assembly line is planned for the grafting of grape cuttings. This line will be equipped with two conveyor belts, a delta robot, and a computer vision system. The use of computer vision in automating the grafting process for grape cuttings offers significant benefits in terms of efficiency, quality improvement, and cost reduction. By incorporating image processing algorithms and advanced robotics, this technology has the potential to revolutionize the viticulture industry. Thanks to training a computer vision system to analyze data on rootstock and graft grape varieties, it is possible to reduce the number of defects by half. The implementation of a semi-automated computer vision system can improve crossbreeding efficiency by 90%. Reducing the time spent on pairing selection is also a significant advantage. While manual selection takes between 1 and 2 min, reducing the time to 30 s using the semi-automated system, and the prospect of further automation reducing the time to 10–15 s, will significantly increase the productivity and efficiency of the process. In addition to the aforementioned benefits, the integration of computer vision technology in grape grafting processes brings several other advantages. One notable advantage is the increased accuracy and precision in pairing selection. Computer vision algorithms can analyze a wide range of factors, including size, shape, color, and structural characteristics, to make more informed decisions when matching rootstock and graft varieties. This can lead to better compatibility and improved overall grafting success rates.

This study investigated the interrelationships between the parameters of density, optical refraction, the volume fraction of ethanol and the total extract, using model solutions and samples of wine materials. The regularities of changes in refractometer readings in the process of alcoholic fermentation have been studied. The functional dependence of the value of the volume fraction of ethanol in the finished wine products on the density and scale of refractometer values has been established. A technique is proposed for controlling the process of alcoholic fermentation of grape must, based on the use of refractometry. Finally, we present an algorithm to calculate the composition (volume fraction of ethanol, mass concentration of the total extract) of the fermentation product from its physical properties (density, refractive index), the coefficient of determination was R2 = 0.975.

The aim of the study was to develop a methodology for calculating and optimizing devices for the magnetic exploration of fossils containing materials with a high magnetic permeability. The proposed technique is based on the calculation of electrostatic fields perturbed by conducting bodies and of magnetic fields perturbed by ferromagnets with a high magnetic permeability. It uses an integral equation of the first kind. This technique is preferable to the technique consisting in the use of an integral equation of the second kind, since in the situation under consideration, the latter does not have a unique solution and requires transformation. Prospects for the development of this area allow one to bring geophysical services to the service market on a new scientific and technical production level; reduce the environmental burden on nature by replacing magnetometric measurements with energy-saving, environmentally safe technology; and ensure the export potential of magnetometric equipment.

This paper presents an analysis of modern methods used to determine antioxidant activity. According to research by the World Health Organization, the deficiency of such important nutrients as antioxidants leads to a decrease in body resistance and the development of chronic diseases. When it comes to diet, the inclusion of foods with a high content of antioxidants helps to increase life expectancy. As a result of this research, the mass concentration of phenolic substances and the antioxidant activity of phenolic antioxidants in young white and red table wine materials were determined using amperometric and chemiluminescent methods in order to determine antioxidant activity. Regression equations reflecting the relationship between the indicator of antioxidant activity and the value of the mass concentration of phenolic substances in young table wine materials were derived. The conversion coefficient for determining the mass concentration of phenolic substances when using Trolox-C and gallic acid as standards was established, which was—3.75. Based on a multiple linear regression model, the total antioxidant activity of the samples (F9.5 = 19.10 and p = 0.0023) can be fairly accurately predicted with an R2 of 0.921 for the calibration data set. A neural network regression model (NNRM) was chosen for the machine-learning regression analysis of the antioxidant activity of the wine samples due to its effectiveness in predicting outcomes in various applications. The implementation was performed using the fitrnet function provided in the Statistics and Machine Learning Toolbox in MATLAB R2021b. The MSE of the calibration model was 0.056; however, the MSE for the three validation samples was much higher, at 0.272.