Technical vision in grain sorting and analysis

The review analyzes current methods of applying technical vision to automate seed analysis and sorting processes, including grain crops. Various approaches are considered, ranging from simple image processing methods to complex systems that integrate machine learning, spectroscopy and advanced optical solutions. Special attention is given to assessing seed maturity and quality, improving the accuracy and efficiency of sorting systems, and utilizing modern object detection algorithms, such as YOLOv5n. Key factors influencing sorting efficiency are analyzed, including image analysis methods, machine learning algorithms, hardware implementations, and database creation. The results of this review can serve as a foundation for developing and improving technical vision-based separators for agricultural production.