{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Industrial Engineering, Faculty of Engineering-Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia",
    "Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Adnan Miski",
      "orcid": "0009-0006-2321-7802"
    },
    {
      "affs": [
        1
      ],
      "name": "Moayad Alshangiti"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e19kzx02jfy0a.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0349608",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13712758/",
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  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
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  "native_subjects": [
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      "heading",
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  "pmcid": "PMC13712758",
  "pmid": "42945366",
  "published": "2026-10-27",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/e19kzx02jfy0a.ris",
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    {
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  "title": "Lightweight CNN and transformer detectors for real-time single-fruit date-variety recognition on edge GPU hardware",
  "title_text": "Lightweight CNN and transformer detectors for real-time single-fruit date-variety recognition on edge GPU hardware",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:23:16Z",
  "url": "https://research.draconitic.com/paper/e19kzx02jfy0a",
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