Machine Vision Methods and Their Application for Wear Analysis of Tillage Implement Tools

The growing demands on the reliability and efficiency of agricultural machinery make the task of studying the wear of its implements increasingly urgent. The paper presents a review of the potential applications of machine vision technologies for analyzing the wear of structural elements in soil tillage tools. Key approaches, such as 3D surface reconstruction, image processing, and deep learning methods, are examined. We analyze the prospects of automating the assessment of part conditions, eliminating the subjective factor in diagnostics, and improving measurement accuracy. Particular attention is given to the technological capabilities of machine vision, its advantages over traditional analysis methods, and potential areas for further research to increase the service life of the working parts of soil tillage machines.