GLL-YOLO: A Lightweight Network for Detecting the Maturity of Blueberry Fruits

Yanlei Xu, Haoxu Li, Yang Zhou, Yuting Zhai, Yang Yang and Daping Fu ()
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Yanlei Xu: College of Information and Technology, Jilin Agricultural University, Changchun 130118, China
Haoxu Li: College of Information and Technology, Jilin Agricultural University, Changchun 130118, China
Yang Zhou: College of Information and Technology, Jilin Agricultural University, Changchun 130118, China
Yuting Zhai: College of Information and Technology, Jilin Agricultural University, Changchun 130118, China
Yang Yang: College of Information and Technology, Jilin Agricultural University, Changchun 130118, China
Daping Fu: College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China

Agriculture, 2025, vol. 15, issue 17, 1-21

Abstract: The traditional detection of blueberry maturity relies on human experience, which is inefficient and highly subjective. Although deep learning methods have improved accuracy, they require large models and complex computations, making real-time deployment on resource-constrained edge devices difficult. To address these issues, a GLL-YOLO method based on the YOLOv8 network is proposed to deal with problems such as fruit occlusion and complex backgrounds in mature blueberry detection. This approach utilizes the GhostNetV2 network as the backbone. The LIMC module is suggested to substitute the original C2f module. Meanwhile, a Lightweight Shared Convolution Detection Head (LSCD) module is designed to build the GLL-YOLO model. This model can accurately detect blueberries at three different maturity stages: unripe, semi-ripe, and ripe. It significantly reduces the number of model parameters and floating-point operations while maintaining high accuracy. Experimental results show that GLL-YOLO outperforms the original YOLOv8 model in terms of accuracy, with mAP improvements of 4.29%, 1.67%, and 1.39% for unripe, semi-ripe, and ripe blueberries, reaching 94.51%, 91.72%, and 93.32%, respectively. Compared to the original model, GLL-YOLO improved the accuracy, recall rate, and mAP by 2.3%, 5.9%, and 1%, respectively. Meanwhile, GLL-YOLO reduces parameters, FLOPs, and model size by 50%, 39%, and 46.7%, respectively, while maintaining accuracy. This method has the advantages of a small model size, high accuracy, and good detection performance, providing reliable support for intelligent blueberry harvesting.

Keywords: blueberry; deep learning; lightweight; maturity detection; edge devices (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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