Strawberry leaf disease detection algorithm based on dynamic adaptive feature network

doi:10.3969/j.issn.1004-1524.20250271

Acta Agriculturae Zhejiangensis ›› 2026, Vol. 38 ›› Issue (8): 1660-1673.DOI: 10.3969/j.issn.1004-1524.20250271

• Biosystems Engineering • Previous Articles Next Articles

Strawberry leaf disease detection algorithm based on dynamic adaptive feature network

YU Fengjun¹(), ZHOU Xiaoping²^,^*(), GONG Tanfei³

¹ Department of Information Technology, Henan Judicial Police Vocational College, Zhengzhou 450046, China
² School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
³ College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China

Received:2025-04-01 Online:2026-08-25 Published:2026-09-14

Abstract

Abstract:

Aiming at the problems of complex detection scenarios and difficult detection of small-target diseases in strawberry leaf disease detection under natural environments, we propose a strawberry leaf disease detection algorithm based on a dynamic adaptive feature network (DAFNet) in this paper. Firstly, a multi-dimensional feature attention (MFA) mechanism is constructed in the backbone network to strengthen the model’s attention to disease features. Secondly, a semantic refinement module (SRM) and a texture refinement module (TRM) are designed in the neck network to enhance the model’s capability of extracting detailed information under complex backgrounds. Furthermore, the DySample upsampling module is adopted to adaptively optimize multi-level feature maps, so as to improve the recognition accuracy of small-scale disease targets. Experimental results demonstrate that the proposed DAFNet achieves a mean average precision (mAP) of 91.6% on the strawberry leaf disease dataset, which is 2.0 percentage points higher than the baseline model. Meanwhile, it maintains favorable real-time detection performance, providing an effective solution for practical strawberry leaf disease detection tasks.

Key words: strawberry leaf disease, target detection, attention mechanism, DySample upsampling module

CLC Number:

YU Fengjun, ZHOU Xiaoping, GONG Tanfei. Strawberry leaf disease detection algorithm based on dynamic adaptive feature network[J]. Acta Agriculturae Zhejiangensis, 2026, 38(8): 1660-1673.

Figures/Tables 13

References 28

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类别 Category	数量分布Quantity distribution
类别 Category	训练集 Training set	验证集 Validation set	测试集 Test set
角叶斑病Angular leaf spot	13 657	3 965	1 951
钙素缺乏症Calcium deficiency	3 125	890	446
叶斑病Leaf spot	12 254	3 546	1 756
白粉病Powdery mildew	9 415	2 687	1 339
总计Total	38 451	11 088	5 492

类别 Category	数量分布Quantity distribution
类别 Category	训练集 Training set	验证集 Validation set	测试集 Test set
角叶斑病Angular leaf spot	13 657	3 965	1 951
钙素缺乏症Calcium deficiency	3 125	890	446
叶斑病Leaf spot	12 254	3 546	1 756
白粉病Powdery mildew	9 415	2 687	1 339
总计Total	38 451	11 088	5 492

注意力机制 Attention mechanism	P/%	R/%	mAP/%	IS/s^-1
无Additive free	90.7	89.3	90.4	60.3
CBAM	90.1	86.8	89.3	58.4
BAM	89.8	86.5	89.1	58.7
EMA	91.8	88.7	90.7	58.3
MFA	92.7	90.9	91.6	58.2

注意力机制 Attention mechanism	P/%	R/%	mAP/%	IS/s^-1
无Additive free	90.7	89.3	90.4	60.3
CBAM	90.1	86.8	89.3	58.4
BAM	89.8	86.5	89.1	58.7
EMA	91.8	88.7	90.7	58.3
MFA	92.7	90.9	91.6	58.2

MFA	SRM+TRM	DySample	P/%	R/%	mAP/%	IS/s^-1	Params/10⁶	FLOPs/10⁹
×	×	×	89.8	88.9	89.6	60.3	31.8	79.0
√	×	×	91.2	89.8	90.9	58.5	32.2	80.3
×	√	×	90.9	89.5	90.4	58.8	32.0	80.1
×	×	√	90.4	89.2	89.9	59.3	31.9	79.8
√	√	×	91.8	90.3	91.1	57.9	32.4	81.2
×	√	√	91.3	90.1	91.2	58.0	32.2	80.9
√	×	√	91.5	90.5	91.4	58.2	32.3	81.2
√	√	√	92.7	90.9	91.6	57.3	32.5	81.5

Strawberry leaf disease detection algorithm based on dynamic adaptive feature network

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网络模型Network model	P	R	mAP/%	IS/s^-1	Params/10⁶	FLOPs/10⁹
Faster R-CNN	86.4	85.1	85.7	6.8	102.7	168.3
SSD	85.6	83.8	84.9	17.6	29.4	99.1
FCOS	89.8	88.9	89.6	60.3	31.8	79.0
YOLOv9-M	88.7	86.8	87.4	71.3	20.0	76.3
YOLOv10-M	90.6	87.2	89.1	72.1	15.4	59.1
YOLOv11-M	90.9	88.1	90.3	70.9	20.1	68.0
KTD-YOLOv8	89.2	86.3	87.5	82.6	13.4	—
MCDCNet	88.5	87.0	88.2	45.2	44.85	184.9
LGM-Net	91.3	90.2	90.8	15.4	82.1	—
YOLOv8n-vegetable	88.2	86.1	88.7	119.1	9.1	—
DAFNet	92.7	90.9	91.6	57.3	32.5	81.5

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