Deep Learning and N8N-Based Soybean Defect Recognition and Traceable Quality Grading System

Rafie Hamizan Al Hafiz (Politeknik Negeri Sriwijaya)
Pola Risma (Politeknik Negeri Sriwijaya)
Hsien-Wei Tseng (Tamkang University)
Chun-Chieh Fan (Saint John’s University)

International Journal of Research in Vocational Studies (IJRVOCAS)
Vol. 6 No. 2 (2026): IJRVOCAS - August

Soybean is an important food commodity whose market value is strongly influenced by seed quality. However, quality inspection in many small and medium agro-industries is still performed manually, making the process slow, subjective, and poorly documented. This study develops an automated soybean inspection system that combines deep-learning-based defect recognition with traceability-supported quality reporting through workflow automation. A YOLOv11n model was trained on a self-collected dataset of 344 annotated images covering five classes, namely Intact, Broken, Skin Damaged, Spotted, and Immature, which was expanded to 1,032 images through augmentation. The model runs on a Raspberry Pi 5 inspection station, where accumulated detection results are converted into a defect rate that determines the quality grade of each batch. An n8n workflow then forwards every inspection result to the Gemini 2.5 Flash large language model to generate a narrative quality-control report, and each batch is stored in an SQLite database that can be accessed through a history viewer to support traceability. Validation results show a precision of 92.73%, a recall of 90.71%, an mAP@0.50 of 96.54%, and an mAP@0.50–0.95 of 88.67%. Functional testing demonstrates that the system produces consistent grades, accumulates detections from multiple captures into a single batch, and generates reports automatically, while still producing a rule-based report when the language model service is unavailable. The proposed system therefore offers an automated, low-cost, and traceability-supported approach to soybean quality inspection that is suitable for deployment on edge devices.