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Open AccessArticle
BMDNet-YOLO: A Lightweight and Robust Model for High-Precision Real-Time Recognition of Blueberry Maturity
by
Huihui Sun
Huihui Sun 1 and
Rui-Feng Wang
Rui-Feng Wang 2,*
1
School of Mechanical and Electrical Engineering, Huainan Normal University, Huainan 232038, China
2
Department of Crop and Soil Sciences, College of Agriculture and Environmental Sciences, University of Georgia, Tifton, GA 31793, USA
*
Author to whom correspondence should be addressed.
Horticulturae 2025, 11(10), 1202; https://doi.org/10.3390/horticulturae11101202 (registering DOI)
Submission received: 2 September 2025
/
Revised: 30 September 2025
/
Accepted: 4 October 2025
/
Published: 5 October 2025
Abstract
Accurate real-time detection of blueberry maturity is vital for automated harvesting. However, existing methods often fail under occlusion, variable lighting, and dense fruit distribution, leading to reduced accuracy and efficiency. To address these challenges, we designed a lightweight deep learning framework that integrates improved feature extraction, attention-based fusion, and progressive transfer learning to enhance robustness and adaptability To overcome these challenges, we propose BMDNet-YOLO, a lightweight model based on an enhanced YOLOv8n. The backbone incorporates a FasterPW module with parallel convolution and point-wise weighting to improve feature extraction efficiency and robustness. A coordinate attention (CA) mechanism in the neck enhances spatial-channel feature selection, while adaptive weighted concatenation ensures efficient multi-scale fusion. The detection head employs a heterogeneous lightweight structure combining group and depthwise separable convolutions to minimize parameter redundancy and boost inference speed. Additionally, a three-stage transfer learning framework (source-domain pretraining, cross-domain adaptation, and target-domain fine-tuning) improves generalization. Experiments on 8,250 field-collected and augmented images show BMDNet-YOLO achieves 95.6% mAP@0.5, 98.27% precision, and 94.36% recall, surpassing existing baselines. This work offers a robust solution for deploying automated blueberry harvesting systems.
Share and Cite
MDPI and ACS Style
Sun, H.; Wang, R.-F.
BMDNet-YOLO: A Lightweight and Robust Model for High-Precision Real-Time Recognition of Blueberry Maturity. Horticulturae 2025, 11, 1202.
https://doi.org/10.3390/horticulturae11101202
AMA Style
Sun H, Wang R-F.
BMDNet-YOLO: A Lightweight and Robust Model for High-Precision Real-Time Recognition of Blueberry Maturity. Horticulturae. 2025; 11(10):1202.
https://doi.org/10.3390/horticulturae11101202
Chicago/Turabian Style
Sun, Huihui, and Rui-Feng Wang.
2025. "BMDNet-YOLO: A Lightweight and Robust Model for High-Precision Real-Time Recognition of Blueberry Maturity" Horticulturae 11, no. 10: 1202.
https://doi.org/10.3390/horticulturae11101202
APA Style
Sun, H., & Wang, R.-F.
(2025). BMDNet-YOLO: A Lightweight and Robust Model for High-Precision Real-Time Recognition of Blueberry Maturity. Horticulturae, 11(10), 1202.
https://doi.org/10.3390/horticulturae11101202
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