Development of an Intelligent Inspection System Based on YOLOv7 for Real-Time Detection of Foreign Materials in Fresh-Cut Vegetables

Hary Kurniawan, Muhammad Akbar Andi Arief, Braja Manggala, Hangi Kim, Sangjun Lee, Moon S. Kim, Insuck Baek and Byoung-Kwan Cho ()
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Hary Kurniawan: Department of Smart Agricultural System, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea
Muhammad Akbar Andi Arief: Department of Smart Agricultural System, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea
Braja Manggala: Department of Smart Agricultural System, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea
Hangi Kim: Department of Biosystem Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea
Sangjun Lee: Department of Biosystem Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea
Moon S. Kim: Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA
Insuck Baek: Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA
Byoung-Kwan Cho: Department of Smart Agricultural System, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea

Agriculture, 2025, vol. 15, issue 21, 1-26

Abstract: Ensuring food safety in fresh-cut vegetables is essential due to the frequent presence of foreign material (FM) that threatens consumer health and product quality. This study presents a real-time FM detection system developed using the YOLO object detection framework to accurately identify diverse FM types in cabbage and green onions. A custom dataset of 14 FM categories—covering various shapes, sizes, and colors—was used to train six YOLO variants. Among them, YOLOv7x demonstrated the highest overall accuracy, effectively detecting challenging objects such as transparent plastic, small stones, and insects. The system, integrated with a conveyor-based inspection setup and a Python graphical interface, maintained stable and high detection accuracy confirming its robustness for real-time inspection. These results validate the developed system as an alternative intelligent quality-control layer for continuous, automated inspection in fresh-cut vegetable processing lines, and establish a solid foundation for future robotic-based removal systems aimed at fully automated food safety assurance.

Keywords: foreign materials; fresh-cut vegetables; deep learning; YOLOv7 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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