In recent years, artificial intelligence technologies have been developing more and more rapidly, and a lot of research is aimed at solving the problem of explainable artificial intelligence. Various XAI methods are being developed to allow the user to understand the logic of how machine learning models work, and in order to compare the methods, it is necessary to evaluate them. The paper analyzes various approaches to the evaluation of XAI methods, defines the requirements for the evaluation system and suggests metrics to determine the various technical characteristics of the methods. A study was conducted, using these metrics, which determined the degradation in the explanation quality of the SHAP and LIME methods with increasing correlation in the input data. Recommendations are also given for further research in the field of practical implementation of metrics, expanding the scope of their use.

Background: This study investigated the ultraviolet (UV) absorption spectra of various types and ages of grape wines and the correlation these spectra presented with their phenolic constituents. Firstly, the differences in UV spectra were characterized for different wine samples, depending on their type and age. Methods: The following methods were used in this study: ultraviolet visible spectrophotometry, Folin–Ciocalteu spectrophotometric method, high-performance liquid chromatography. Results: Then, it was demonstrated that for identically aged wines, the 280 nm absorbance is proportional to the concentration of phenolic compounds, as determined by the Folin–Ciocalteu method. Next, an investigation was conducted into the absorption coefficients of different phenolic classes commonly found in grapes and wine. Finally, the range in variation of phenolic compounds in various types of grape wines was established. Conclusions: This work provides a methodological approach to rapidly determine the concentration of phenolic compounds in wines using UV spectroscopy, provided that their age is known. As UV spectrophotometers are available in nearly all laboratories, this may provide a cheaper and faster alternative to current methods, including high-performance liquid chromatography (HPLC).

Plant health plays an important role in influencing agricultural yields and poor plant health can lead to significant economic losses. Grapes are an important and widely cultivated plant, especially in the southern regions of Russia. Grapes are subject to a number of diseases that require timely diagnosis and treatment. Incorrect identification of diseases can lead to large crop losses. A neural network deep learning dataset of 4845 grape disease images was created. Eight categories of common grape diseases typical of the Black Sea region were studied: Mildew, Oidium, Anthracnose, Esca, Gray rot, Black rot, White rot, and bacterial cancer of grapes. In addition, a set of healthy plants was included. In this paper, a new selective search algorithm for monitoring the state of plant development based on computer vision in viticulture, based on YOLOv5, was considered. The most difficult part of object detection is object localization. As a result, the fast and accurate detection of grape health status was realized. The test results showed that the accuracy was 97.5%, with a model size of 14.85 MB. An analysis of existing publications and patents found using the search “Computer vision in viticulture” showed that this technology is original and promising. The developed software package implements the best approaches to the control system in viticulture using computer vision technologies. A mobile application was developed for practical use by the farmer. The developed software and hardware complex can be installed in any vehicle. Such a mobile system will allow for real-time monitoring of the state of the vineyards and will display it on a map. The novelty of this study lies in the integration of software and hardware. Decision support system software can be adapted to solve other similar problems. The software product commercialization plan is focused on the automation and robotization of agriculture, and will form the basis for adding the next set of similar software.

The relationship between individual phytochemical constituents and overall antioxidant capacity or total phenolic content (TPC) is poorly understood in faba bean. This study used a range of linear and nonlinear regression techniques to investigate whether the antioxidant capacity and TPC of 60 faba bean samples (flour and methanolic extracts) could be predicted from 12 individual compounds (10 common polyphenols and 2 alkaloid glycosides) measured in the same samples. Nonlinear regression using machine learning with a Radial Basis Function showed the best performance for antioxidant and TPC prediction across all sample types, while multiple linear regression allowed moderately accurate predictions in most sample matrices. Improved performance metrics were seen for the methanolic extracts compared to the flour samples. The strongest predictors of antioxidant activity in the multiple linear regression models were protocatechuic acid, p-hydroxybenzoic acid, and ferulic acid, suggesting that these compounds are particularly important contributors to the high antioxidant activity of faba bean. Understanding the relationship between individual constituents and the antioxidant capacity may help food technologists and plant breeders develop faba bean products with maximal health benefits.

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.

The recent steady demand for products with increased antioxidant properties largely determines the direction of research in the field of creating high‐quality products with increased biological value. Insufficient knowledge of the antioxidant activity and potential biological value of whiskey makes this area of research relevant and in demand. This study presents the results of an analysis of the influence of distillate aging conditions on the phenolic composition and antioxidant capacity of whiskey, obtained using coulometric, chromatographic, and amperometric methods of analysis. During the study, samples of whiskey with increased antioxidant capacity were obtained based on the use of alcoholic extracts of grape stems of the Vitis vinifera species. The data obtained as a result of the study can be used in the future both to improve the efficiency of methods for monitoring and regulating the circulation of alcoholic products based on the developed methodology for determining antioxidant capacity and to contribute to the creation of a new type of alcoholic products with an increased content of bioavailable biologically active substances and antioxidant capacity.