The benefits of automated sorting are significant when compared to manual methods. Automated systems can handle large volumes of vegetables in shorter periods, increasing processing speed and reducing delays caused by fatigue and human error. They also provide consistent and objective grading based on predetermined criteria, whereas manual sorting may vary due to individual subjectivity. Moreover, automated systems can incorporate multiple sorting criteria simultaneously, whereas manual sorting is limited to basic assessments. From a labor and cost perspective, automation reduces the need for a large workforce, resulting in operational savings and improved reliability. Additionally, automated sorting systems can collect and analyze data to monitor performance and continuously improve efficiency, an advantage not readily available with manual methods.

This study focuses on the design and development of an eggplant automatic grading and sorting machine using machine learning. By implementing this system, labor costs can be reduced, productivity and accuracy can be increased, and the overall efficiency of vegetable grading and sorting can be enhanced, particularly in agricultural operations.