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..

This article shows the method and results of experimental RESEARCHES of machines for egg sorting into categories based on size and form using a vision system. The principal difference of the considered machine for automatic sorting of eggs into categories from existing sorting machines is the division of eggs into categories by size and the separation of substandard eggs in the flow. Experimental verification showed that the machine provides the division of eggs in size into four categories and the separation of eggs of irregular shapes and sizes in the flow. The accuracy of separation of eggs into categories depending on the performance. With a productivity of up to two eggs per second, the accuracy of egg separation averages 94.8%., With a productivity of three eggs per second was 87.4%.