Machine Vision for Coupled Egg Dynamics & Intelligent Sorting

A research initiative focused on developing advanced egg grading and recognition systems.

A placeholder image representing the concept of machine vision in agricultural sorting.

Building upon the findings from the trajectory modeling project, this research initiative, which I also led, aims to develop a comprehensive intelligent sorting system for eggs. The core of this project is to leverage machine vision and data analysis to predict and classify eggs based on their dynamic and morphological characteristics.

Core Activities

  1. Literature Review: We conducted an extensive review of the field, systematically summarizing over 240 recent articles. This covered the coupling mechanisms of egg morphology, contact interface physics, and dynamic rolling behavior.
  2. Manuscript Preparation: Based on the literature review, we drafted an initial review manuscript. This paper establishes the theoretical foundation for our future experimental work.
  3. Future Work Planning: The primary outcome of this initial phase was to lay the groundwork for the research group’s subsequent projects. This includes designing egg dynamics models for rolling behavior prediction and establishing robust egg grading and recognition systems.

Planned Outputs

This foundational work is projected to result in one JCR Q1 review paper. The systems and models developed will serve as a cornerstone for future innovations in automated agricultural processing, enhancing both efficiency and accuracy in quality control.