Res. Agr. Eng., X:X | DOI: 10.17221/29/2026-RAE

Enhancing the performance of lotus seed kernel and shell sorting after dehulling: A real-time approach based on deep learning and image processing (YOLOv8)Original Paper

DINH-TU NGUYEN ORCID...¹, HUU-PHAT TRAN ORCID...², DUC-TIN LE ORCID...¹, HOAI-TAN NGUYEN ORCID...³, THANH-THUONG HUYNH ORCID...³

¹ Faculty of Mechanical Engineering, Can Tho University of Technology, Can Tho, Vietnam

² Department of Mechanical Engineering, College of Engineering, National Central University, Tao Yuan, Taiwan

³ Faculty of Mechanical Engineering, College of Engineering, Can Tho University, Can Tho, Vietnam

Lotus seeds represent a high-value agricultural commodity, widely utilised in the food and pharmaceutical industries for their nutritional and medicinal benefits. However, post-harvest processing, specifically the removal of shells, often relies on manual labour or rudimentary equipment that results in low productivity. This study proposes a computer vision system based on the YOLOv8 deep learning framework to automate and enhance the efficiency of post-decortication sorting. The model is designed to detect and classify three distinct components: unshelled seeds, kernels, and shell fragments. Based on these predictions, the system controls pneumatic actuators to perform precise, real-time separation. Trained on a dataset of more than 30 000 images, the model achieved a mean average precision (mAP@0.5) of 92.6%. These results validate the model’s capability to accurately identify lotus seed components under high-speed processing conditions. Consequently, this research presents a feasible technological solution to mitigate the labour dependence and optimise the shell residue removal stage in industrial lotus seed processing.

Keywords: computer vision; deep learning; fresh lotus seed shelling; post-harvest automation; shell-kernel lotus seed separation; smart and precision agriculture; YOLOv8n

Received: February 8, 2026; Accepted: May 19, 2026; Prepublished online: July 23, 2026