International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

The article presents a comprehensive scientific and engineering development — an automated installation and an algorithmic platform for assessing the quality characteristics of varietal seed potato tubers. The development is aimed at solving key agroengineering problems related to increasing the accuracy, standardization and productivity of tuber analysis processes in seed production.
The methodological basis of the study is a combination of tensometric measurement of tuber mass with computer vision algorithms based on the OpenCV library in the Python programming environment. The algorithm allows for automatic and highly accurate determination of mass, linear dimensions (length, width, height), area and perimeter of the tuber, as well as calculation of derived indicators such as shape index and shape coefficient, which are important for sorting and determining varietal affiliation.
To verify the developed system, experimental studies were conducted on an automated setup using control measurements performed by traditional methods (calipers, electronic scales). The study included both mini-tubers and standard seed tubers of the Alliance potato variety. Statistical analysis of the data showed a high degree of consistency between digital and manual measurements, and also revealed a significant advantage of the automated method in terms of productivity: the analysis time for one tuber was reduced by an average of seven times.
Particular attention is paid to the compliance of the proposed method with national and international standards, in particular, the requirements of GOST 33996–2016, which guarantees the possibility of its practical application in real production conditions. The authors substantiate that the introduction of digital technologies in the process of sorting seed potatoes allows minimizing subjective errors, reducing labor costs, increasing the speed of data processing and standardizing the quality assessment process.
The scientific novelty of the work lies in the integration of algorithmic data processing with physical measurements on one platform, which allows for the implementation of a comprehensive agro-engineering system for solving problems of selection, seed production and automated sorting. The developed installation can be adapted and scaled for use with other fruit and vegetable crops, which opens up opportunities for further research and expansion of the range of applications.
The results of the study are relevant for agro-industrial enterprises, research institutes, seed farms and agricultural machinery manufacturers. The proposed system can be integrated into existing sorting and processing lines, ensuring the transition to precision agriculture and industry 4.0 technologies in the agricultural sector..

A prototype of a system for segmenting images of trains and wagons has been developed. Video cameras and specialized websites are used as the source of the original images. Median filtering of images and increase of their local contrast is carried out. The contours of the objects were calculated using the Sobel and Canny methods. Image segmentation is performed by the method of contour lines. As a result of the processing on the images of trains and wagons, meaningful areas (segments) were identified, for example, windows, headlights, etc. Detection of content areas of the object is performed using fuzzy membership functions. The hardware and software implementation of the computer system is made in Python using scipy and scikit-fuzzy libraries, the Google Colab cloud platform and Raspberry Pi 3B+ microcomputer.

The scope of this paper is using Dijkstra algorithm in Python to get the urban transport optimization one step further, by finding the best route, besides analysing the shortest one. Studying urban transport routes has been first analysed by Dijkstra algorithm taking into consideration two parameters, the number of stations and distances between stations. After this, the code of Dijkstra algorithm has been implemented in Python, adding the demand for travelling in each station.

This paper examines the applicability of the neural networks in developing predictive models. A predictive model based on artificial neural networks has been proposed and training has been simulated by applying the Long Short-Term Memory Neural Network module and the time series method. Python programming language to simulate the neural network was used. The model uses the stochastic gradient descent and optimizes the mean square error. Business indicators for forecasting the results of the activity and the risk of bankruptcy of a company are forecasted and a comparison of the obtained forecast values with the actual ones is performed in order to assess the accuracy of the forecast of the developed model. As a result, it can be noted that business indicators can be successfully predicted through the Long Short-Term Memory Neural Network and the forecasted values are close to the actual ones.

This article is devoted to the problem of a passport for drilling and blasting operations formation, taking into account the main
characteristics. At most mining enterprises, this process is a manual calculation that leads to errors due to human factor and increases the
time it takes to generate drilling and blasting passport, and, as a consequence, the time for drilling and blasting.
The proposed solution is an automated complex that bases its calculations on the data of the cross-section mines shape, the dimensions of
the height and width of the mine and the cross-sectional area in the tunnel, the fortress on the scale of prof. M.M. Protodyakonov and the
thickness of the host rocks. All geometrical parameters of tunnel face are obtained automatically based on laser scanning. For further
calculations, intelligent algorithms are used, implemented using deep learning neural networks (with python tensorflow library). It is worth
noting that the final decision on the acceptance of the drilling and blasting passport is made by the person in charge. The result of using the
proposed system is automatically generated passport of drilling and blasting operations, including its alternative variations (due to the
passport chosen by the person in charge, the system will receive feedback to further improvement of the system algorithm).