Edge Artificial Intelligence for Real-Time Fresh Produce Identification in Retail Weighing Systems

Real-time recognition of loose fresh produce is a key requirement for intelligent retail weighing systems, enabling automated replacement of or assistance to manual PLU-based item selection. However, the deployment performance of recent YOLO architectures on embedded edge platforms such as the NVIDIA Jetson Xavier NX remains insufficiently studied in practical retail scenarios. This study aims to benchmark recent YOLO architectures for real-time fresh produce recognition on embedded edge devices. This work presents an Edge–AI retail weighing system that recognizes Malaysian fresh produce using YOLOv9, YOLOv10, and YOLOv11 models on the Jetson Xavier NX. A domain-specific dataset of 8 450 images across 26 classes was created by merging ImageNet and Roboflow sources and applying quality filtering and unified preprocessing. Each model was fine-tuned and optimized with TensorRT at FP16 and INT8 precision. Transfer learning improved accuracy across all models; YOLOv11-Large achieved the highest mAP@0.5 of ≈ 0.897 but at a reduced frame rate, while the mid-sized YOLOv10-M delivered an mAP@0.5 of ≈ 0.890 with near-real-time performance inference. Inference analysis shows that pre-and post-processing add only a few milliseconds per frame yet become proportionally significant as inference speeds increase; YOLOv11’s Non-Maximum Suppression (NMS) head introduces notable latency relative to YOLOv10’s NMS-free design. Quantized YOLOv10-M and YOLOv10-N sustain ≈ 14–19 FPS , offering the best balance between accuracy and speed. Qualitative tests on market footage confirm robust detection, indicating that these optimized models enable accurate, low-latency produce identification for intelligent retail weighing.

@article{Teo2026,
  title     = {Edge Artificial Intelligence for Real-Time Fresh Produce Identification in Retail Weighing Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {1},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Shi Han Teo and Jun Kit Chaw},
  doi       = {10.14569/IJACSA.2026.0170176},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170176}
}