Apple leaf disease recognition based on improved EfficientNetV2 model

doi:10.16178/j.issn.0528-9017.20240918

Abstract

Abstract:

Apple leaf diseases can seriously affect the production capacity and quality of apples. Accurate identification of diseases is of great significance for effective prevention and control and reducing losses. The lesions of apple leaf diseases vary in size and are distributed in complex and diverse positions on the leaves. Moreover, they are disturbed by the complex background in the natural environment, which leads to problems such as difficulty in identifying leaf diseases and low accuracy. Based on this, this paper proposed an apple leaf disease recognition model (Efficient-BEANet) that improved EfficientNetV2 based on the attention mechanism. Firstly, this study proposed a high-speed dual-channel normalization (BCET) method and established the connection between batch normalization (BN) and layer normalization (LN) methods, enabling the model to integrate feature information of multiple scales and thereby improve the problem of difficult identification of small lesions. Meanwhile, this model was combined with the enhanced linear transformation to accelerate the convergence speed of the model. In addition, by designing a multi-scale shared attention mechanism (ESCA), the correlation between the features of the two dimensions of space and channel was established. Through automatic learning of the features of diseases, the representation of important features was enhanced, and the areas where disease spots appear were given special attention. Finally, by extracting multi-level shared channel features, greater weights were adaptively assigned to important features, and information unrelated to the disease was suppressed, with the aim of effectively improving the problems of false detection and missed detection caused by the irregular spatial distribution of disease spots and the interference of complex backgrounds. The experimental results showed that the accuracy of the improved model on the validation set reached 93.32%, which was increased by 3.49 percentage points compared with the original model, and can be used for the effective identification of apple leaf diseases.

Key words: apple leaf disease, normalization, attention mechanism

CLC Number:

S436.611

ZHANG Peng, LIU Shuo, OUYANG Yu, LI Mengmin. Apple leaf disease recognition based on improved EfficientNetV2 model[J]. Journal of Zhejiang Agricultural Sciences, 2026, 67(1): 115-124.

Figures/Tables 16

References 21

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编号	名称	扩展因子	卷积核(步长)	通道数	层数
—	3×3卷积	—	3×3(2)	24	1
stage 1	EBF-MBConv	1	3×3(1)	24	2
stage 2	EBF-MBConv	4	3×3(2)	48	4
stage 3	EBF-MBConv	4	3×3(2)	64	4
stage 4	EB-MBConv	4	3×3(2)	128	6
stage 5	EB-MBConv	6	3×3(1)	160	9
stage 6	EB-MBConv	6	3×3(2)	256	15
—	1×1卷积层&池化层&全连接层	—	—	1 280	1

编号	名称	扩展因子	卷积核(步长)	通道数	层数
—	3×3卷积	—	3×3(2)	24	1
stage 1	EBF-MBConv	1	3×3(1)	24	2
stage 2	EBF-MBConv	4	3×3(2)	48	4
stage 3	EBF-MBConv	4	3×3(2)	64	4
stage 4	EB-MBConv	4	3×3(2)	128	6
stage 5	EB-MBConv	6	3×3(1)	160	9
stage 6	EB-MBConv	6	3×3(2)	256	15
—	1×1卷积层&池化层&全连接层	—	—	1 280	1

归一化方法	精准度	召回率	F₁值	准确率
批归一化(BN)	89.95	89.73	89.76	89.83
层归一化(LN)	87.69	87.10	87.26	87.28
同步批量归一化(SN)	89.59	88.38	88.68	88.55
实例归一化(IN)	89.55	89.05	89.13	89.18
局部响应归一化(LRN)	89.94	89.18	89.40	89.34
增强线性变换的批量归一化 (BNET)	88.02	87.12	87.36	87.28
批量通道归一化(BCN)	89.10	87.88	88.17	88.07
高速双通道归一化(BCET)	92.36	92.42	92.33	92.53

归一化方法	精准度	召回率	F₁值	准确率
批归一化(BN)	89.95	89.73	89.76	89.83
层归一化(LN)	87.69	87.10	87.26	87.28
同步批量归一化(SN)	89.59	88.38	88.68	88.55
实例归一化(IN)	89.55	89.05	89.13	89.18
局部响应归一化(LRN)	89.94	89.18	89.40	89.34
增强线性变换的批量归一化 (BNET)	88.02	87.12	87.36	87.28
批量通道归一化(BCN)	89.10	87.88	88.17	88.07
高速双通道归一化(BCET)	92.36	92.42	92.33	92.53

类型	名称	参数量/10⁶	精准度/%	召回率/%	F₁值/%	准确率/%
通道注意力	通道注意力机制(SE)	19.25	89.95	89.73	89.76	89.83
	高效通道注意力机制(ECA)	19.25	89.49	88.91	89.02	89.03
	聚集-激发注意力机制(GE)	24.51	88.39	88.20	88.61	89.76
归一化注意力	非局部注意力机制(NAM)	19.32	88.50	88.09	88.19	88.24
无参数注意力	简单注意力机制(SimAM)	19.25	76.74	74.23	74.27	74.09
空间与通道注意力	高效多尺度注意力机制(EMA)	19.66	88.31	87.49	87.66	87.59
	卷积块注意力机制(CBAM)	24.42	86.39	86.17	86.14	86.33
	协调注意力机制(CA)	26.99	88.94	88.59	88.71	88.71
	多尺度共享注意力机制(ESCA)	24.96	92.13	92.13	92.14	92.26