Towards Precise Papaya Ripeness Assessment: A Deep Learning Framework with Dynamic Detection Heads
Haohai You,
Jing Fan,
Dongyan Huang,
Weilong Yan,
Xiting Zhang,
Zhenke Sun,
Hongtao Liu and
Jun Yuan ()
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Haohai You: College of Information and Technology & Smart Agriculture Research Institute, Jilin Agricultural University, Changchun 130118, China
Jing Fan: College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China
Dongyan Huang: College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China
Weilong Yan: College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China
Xiting Zhang: College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, China
Zhenke Sun: College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China
Hongtao Liu: College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China
Jun Yuan: College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China
Agriculture, 2025, vol. 15, issue 15, 1-19
Abstract:
Papaya ripeness identification is a key task in orchard management. To achieve efficient deployment of this task on edge computing devices, this paper proposes a lightweight detection model, ABD-YOLO-ting, based on YOLOv8. First, the width factor of YOLOv8n is adjusted to construct a lightweight backbone network, YOLO-Ting. Second, a low-computation ADown module is introduced to replace the standard downsampling structure, aiming to enhance feature extraction efficiency. Third, an enhanced BiFPN is integrated into the neck structure to achieve efficient multi-scale feature fusion. Finally, to strengthen the model’s capability in identifying small objects, the dynamic detection head DyHead is introduced to improve ripeness recognition accuracy. On a self-constructed Japanese quince orchard dataset, ABD-YOLO-ting achieves a mAP50 of 94.7% and a mAP50–95 of 77.4%, with only 1.47 M parameters and 5.4 G FLOPs, significantly outperforming mainstream models such as YOLOv5, YOLOv8, and YOLOv11. On edge devices, the model achieves a well-balanced trade-off between detection speed and accuracy, demonstrating strong potential for practical applications in intelligent harvesting and orchard management.
Keywords: YOLOv8; papaya; DyHead; precision agriculture; ripeness detection (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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