Authors: Jiajun Xu | Yuzhen Lu | Boyang Deng
Source: Journal of the ASABE (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Design, Prototyping, and Evaluation of A New Machine Vision-Based Automated Sweetpotato Grading and Sorting System Highlights A computer algorithm pipeline with YOLOv8 was developed for grading sweetpotatoes for size and surface defects. Pneumatically powered sorting mechanisms were prototyped and evaluated for sweetpotato sorting. The sorting mechanisms achieved 96.9% accuracy and 92.5% repeatability at a conveyor speed of 12 cm s-1. The integrated system achieved online, automated grading and sorting accuracies of 93.8% and 92.7%, respectively. Abstract. Automated quality grading and sorting of fresh market sweetpotato roots have not been achieved at packing facilities, which consequently requires significant labor to manually grade the products and sort them by hand. Given an increasing shortage of labor and rising labor costs, there is a pressing need to develop automated grading and sorting technology for sweetpotato packers to stay competitive and profitable. This study reports on the design, prototyping, and evaluation of an innovative machine vision-based, automated sweetpotato grading and sorting system empowered by state-of-the-art artificial intelligence. The developed prototype consists of a custom-designed roller conveyor-based machine vision unit that performs size and surface defect detection and grading of sweetpotatoes traveling and rotating on the conveyor and pneumatically powered sorting mechanisms for automated sorting of the sweetpotatoes into three grades after they exit from the vision area. A computer algorithm pipeline was developed to segment and track individual sweetpotato instances by YOLOv8l in real-time and perform quality grading based on multiple views of samples for size (length and width) and surface defects, and then activate the sorting mechanisms to selectively segregate graded sweetpotatoes online. Experiments were conducted on the performance of the sorting mechanisms, the machine vision unit, and the fully integrated system at varied conveyor speeds. The sorter without vision integration achieved an accuracy of 96.9% and repeatability of 92.5% at a conveyor speed of 12 cm s -1 . The machine vision system yielded an overall accuracy of 95.8% and 93.8% in grading three-class sweetpotatoes for surface defects alone and for both size and surface defects, respectively, at the conveyor speed of 12 cm s -1 . An overall sorting accuracy of 92.7% was achieved by the integrated system at the conveyor speed of 12 cm s -1 . Grading and sorting accuracies tended to decrease as the conveyor speed increased. A video demo of automated sweetpotato grading and sorting processes is available at https://youtu.be/3QEp9wFTiu0. With more dedicated efforts on hardware and software improvements, the developed grading and sorting machine prototype promises to evolve into a practical implementation at commercial sweetpotato packing facilities. Keywords: Artificial intelligence, Automated grading and sorting, Machine vision, Sweetpotato, System integration. Sorting ; Grading (engineering) ; Rapid prototyping ; Computer science ; Machine vision ; Artificial intelligence ; Engineering drawing ; Computer vision ; Computer graphics (images) ; Engineering ; Programming language Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI