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Transfer learning models for wheat ear detection on multi-source dataset.

Željana Grbović1, Marko Panić2, Dimitrije Stefanović2

  • 1BioSense Institute, University of Novi Sad, Novi Sad, Serbia. zeljanagrbovic@biosense.rs.

Scientific Reports
|December 30, 2025
PubMed
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Researchers developed the BioS-Wheat dataset and evaluated deep learning models for automated wheat ear detection. This aids in accurate, early-stage yield prediction for global food security.

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Data Science

Background:

  • Accurate wheat yield forecasting is vital for global food security, yet current methods are often biased or labor-intensive.
  • Manual counting of wheat ears is a precise but time-consuming method for yield prediction.
  • Automated wheat ear detection can improve the accuracy and efficiency of yield estimation.

Purpose of the Study:

  • To introduce the BioS-Wheat dataset, a novel, high-quality RGB smartphone image dataset for wheat ear detection.
  • To evaluate the performance of six deep learning models for automated wheat ear detection.
  • To provide a baseline for future research in automated wheat yield prediction.

Main Methods:

  • Creation of the BioS-Wheat dataset: 5,696 annotated RGB images across four wheat varieties with high sowing density and minimal row spacing.
Keywords:
Bios-Wheat datasetMask-RCNN based modelsVision transformer based modelWheat ear detectionYolov8

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  • Evaluation of six deep learning models, including RetinaNet, YOLOv8, and RT-DETR, for wheat ear detection.
  • Performance assessment using mean Average Precision (mAP@50).

    • Main Results:

    • RetinaNet, YOLOv8, and RT-DETR achieved the highest mAP@50 of 91% for wheat ear detection.
    • These top-performing models exhibited significantly higher computational complexity.
    • The BioS-Wheat dataset's complexity, due to dense arrangements and occlusion, challenges model robustness.

    Conclusions:

    • The BioS-Wheat dataset offers a valuable resource for developing robust automated wheat ear detection models.
    • Deep learning models show high potential for accurate, early-stage wheat yield prediction.
    • Agronomic diversity in datasets is crucial for enhancing model performance across various conditions.