The recognition and detection of tobacco stems based on the improved YOLOv8n model

doi:10.3969/j.issn.1004-1524.20240508

Acta Agriculturae Zhejiangensis ›› 2025, Vol. 37 ›› Issue (11): 2395-2407.DOI: 10.3969/j.issn.1004-1524.20240508

• Biosystems Engineering • Previous Articles Next Articles

The recognition and detection of tobacco stems based on the improved YOLOv8n model

FENG Yongxin¹(), JI Yuanpeng², CUI Ying¹, BAI Li¹, WANG Jian¹, DING Jia¹, ZHANG Xiaodong¹, ZHANG Weiwei², LI Meng³, ZHANG Weizheng²^,^*()

1. Hebei Tobacco Industry Co., Ltd., Shijiazhuang 050051, China
2. School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450001, China
3. School of Tobacco Science and Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China

Received:2024-06-11 Online:2025-11-25 Published:2025-12-08

Abstract

Abstract:

Real-time and accurate recognition of tobacco stems is essential for enhancing the quality and efficiency of tobacco stem sorting. To tackle the challenges of low efficiency and subpar quality in manual tobacco stem sorting, this paper proposed a tobacco stem recognition method based on the improved YOLOv8n model, aiming to achieve automated detection of tobacco stem structures. Taking the tobacco stems after re-roasting as the research object, variable kernel convolution was used instead of standard convolution based on the YOLOv8n model to enhance the feature extraction of tobacco stems by the network model and reduce parameters of the model. A triple attention module is embedded into the backbone network to enhance the network’s focus on tobacco stem positional information through cross-dimensional interactions. The results demonstrated that the improved model achieved an mAP of 95.40% with a parameter of 2.52×10⁶, floating-point operations per second (FLOPs) of 7.50 G, and detection speed of 62.38 frames per second. Compared to the YOLOv8n model, the improved model achieved a 4.55 percentage points increase in mAP while reducing parameters and FLOPs by 0.49×10⁶ and 0.70 G, respectively. Compared with Faster R-CNN, SSD, YOLOv5n, YOLOv6n, and YOLOv7tiny, the improved model had superior accuracy and faster detection speed, demonstrating superior-comprehensive performance. Furthermore, embedding the improved model into computing devices and testing its performance in real-world applications. The model demonstrated high accuracy and real-time capabilities, effectively improving the efficiency of recognition and sorting of tobacco stems.

Key words: tobacco stem, improved YOLOv8n model, recognition, detection, sorting

CLC Number:

S572

FENG Yongxin, JI Yuanpeng, CUI Ying, BAI Li, WANG Jian, DING Jia, ZHANG Xiaodong, ZHANG Weiwei, LI Meng, ZHANG Weizheng. The recognition and detection of tobacco stems based on the improved YOLOv8n model[J]. Acta Agriculturae Zhejiangensis, 2025, 37(11): 2395-2407.

Figures/Tables 11

Fig.1 The platform for recognition and classification of tobacco stems

Fig.2 Images of tobacco stems

Fig.3 Architecture of the improved YOLOv8n model YGshort, short tobacco stems; YGlong, long tobacco stems. The subsequent numbers indicating the probability of being classified into each category.

Fig.4 Structure of alterable kernel convolution C represents the number of channels, H and W represent the height and width of the tobacco stem images, respectively, the same as Fig.5. N represents the size of the convolution kernel.

Fig.5 Structure of triplet attention module

Table 1 Results of improved YOLOv8n model and six different models on the test set of the tobacco stem

模型 Model	AP_long/%	AP_short/%	AP_broken/%	mAP/%	参数量 Parameter/10⁶	FLOPs/G	FPS
Faster R-CNN	76.09	86.12	57.23	73.15	136.69	200.84	13.22
SSD	79.49	91.74	92.45	87.89	23.61	136.59	29.58
YOLOv5n	90.50	83.93	85.37	86.60	1.90	4.50	63.16
YOLOv6n	89.25	83.46	82.95	85.22	4.70	11.40	52.43
YOLOv7tiny	84.32	83.51	82.97	83.60	6.01	13.00	50.40
YOLOv8n	91.21	89.63	91.72	90.85	3.01	8.20	56.65
改进的YOLOv8n	95.60	94.71	95.89	95.40	2.52	7.50	62.38
Improved YOLOv8n

Fig.6 Comprehensive performance comparison and analysis of seven models APlong, Average precision of long tobacco stem; APshort, Average precision of short tobacco stem; APbroken, Average precision of broken tobacco stem; FLOPs, Floating-point operation per second; FPS, Frames per second.

Fig.7 Comparison of detection performance of seven models The yellow dashed box indicates false detection. YGbroken, Broken tobacco stems; YGshort, Short tobacco stems, YGlong, Long tobacco stems. The subsequent numbersindicating the probability of being classified into each category.

Table 2 Results of ablation experiments on different optimization modules

YOLOv8n模型 YOLOv8n model	AKConv	TAM	AP_long/%	AP_short/%	AP_broken/%	mAP/%	参数量 Parameter/10⁶	FLOP_S/G	FPS
√			91.21	89.63	91.72	90.85	3.01	8.20	56.65
√	√		92.06	91.53	92.83	92.14	2.52	7.40	64.10
√	√	√	95.60	94.71	95.89	95.40	2.52	7.50	62.38

Fig.8 Workflow of recognition and sorting of tobacco stems

Table 3 Comparison between improved YOLOv8n model and manual sorting

方法 Method	长烟梗质量 Quality of long tobacco stem/g	短烟梗质量 Quality of short tobacco stem/g	碎烟梗质量 Quality of broken tobacco stem/g	长梗率 Long tobacco stem rate/%	碎梗率 Broken tobacco stem rate/%	长烟梗识别误差 Identification error of long tobacco stem/%	短烟梗识别误差 Identification error of short tobacco stem/%	碎烟梗识别误差 Identification error of broken tobacco stem/%
人工分选	986.2	413.5	100.3	65.75	6.69	0	0	0
Manual sorting
改进的YOLOv8n模型	982.7	420.4	96.9	65.51	6.46	0.35	1.82	3.40
Improved YOLOv8n model

References 19

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The recognition and detection of tobacco stems based on the improved YOLOv8n model

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