Lightweight CNN and transformer detectors for real-time single-fruit date-variety recognition on edge GPU hardware.

Authors

Adnan Miski, Moayad Alshangiti

Published in

PloS one. Volume 21. Issue 10. Pages e0349608. Epub Oct 27, 2026.

Abstract

Manual sorting of date fruits is time-consuming and labor-intensive, while automated optical sorting lines remain costly for small- and medium-sized enterprises (SMEs). This study presents a deployment-oriented benchmark of five lightweight object detectors for single-fruit date-variety recognition, namely the Convolutional Neural Network (CNN)-based YOLOv8, YOLOv10, YOLOv11, and YOLOv12 Nano variants and the Transformer-based D-Fine-Nano, with RT-DETR-Large included as a high-capacity accuracy reference. Models were evaluated on 1,658 controlled-environment images of nine commercial varieties using grouped 5-fold cross-validation, which kept identified repeat photographs of the same fruit within the same cross-validation fold. Latency was measured on an NVIDIA T4-class edge GPU/industrial-PC accelerator, and the CNN detectors were also exported to Open Neural Network Exchange (ONNX) and TensorRT half-precision (FP16) engines. All Nano models met the 30 frames-per-second threshold for model-side detection latency under the tested conditions and achieved high accuracy, while a Friedman-Nemenyi analysis detected no statistically significant accuracy difference among the lightweight models. Under TensorRT FP16, YOLOv10-Nano achieved the lowest model-side end-to-end latency of 6.27 ms with no observed accuracy degradation. YOLOv8-Nano attained the lowest inference-stage latency of 3.31 ms, but its mean average precision (mAP@50-95) decreased by 1.2 percentage points after TensorRT FP16 conversion. YOLOv12-Nano had the numerically highest mean cross-validation mAP@50-95 and the lowest fold-to-fold variability, albeit at higher latency. These findings characterize model-level detector behavior rather than a complete sorting line. Within that scope, YOLOv10-Nano represents one practical configuration for a T4/TensorRT FP16 pipeline, while the other models offer different speed, stability, and deployment trade-offs, providing SMEs with a well-characterized starting point for the vision component of affordable date-sorting systems.

PMID:
42945366
Bibliographic data and abstract were imported from PubMed on 28 Oct 2026.

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