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

This article examines the use of computer vision and deep learning to automatically determine key quality indicators of apples, enhancing product quality. It describes a digital method for measuring apple size, ripeness, and variety classification using an automated optoelectronic system, achieving an accuracy of at least 86%. Advantages, limitations, and potential productivity benefits for Kazakhstan’s apple production are discussed. An algorithm developed with OpenCV in Python analyzes apple images to determine diameter, height, surface area, red color proportion, and external defects. Tested on “Sinap Almaty” apples, the method measures linear dimensions, crosssectional area, and redness percentage.

The system of the servicing of the machines in the livestock farms has been analyzed and the necessity of optimizing the spare parts services and the scope of the workshops for maintaining the equipment in the livestock breeding. Relations have been developed to optimize the area to be serviced by the workshop and its spare parts warehouse, as well as the optimal average distance to be found from where machines work.