A lightweight tomato leaf disease recognition model integrating a sandglass structure with improved coordinate attention

doi:10.3969/j.issn.1004-1524.20240646

Acta Agriculturae Zhejiangensis ›› 2025, Vol. 37 ›› Issue (1): 217-230.DOI: 10.3969/j.issn.1004-1524.20240646

• Biosystems Engineering • Previous Articles Next Articles

A lightweight tomato leaf disease recognition model integrating a sandglass structure with improved coordinate attention

GU Rui¹^,²(), SONG Cuiling¹, QIAN Chunhua³^,^*()

1. School of Digital Economy and Management, Nanjing University, Nanjing 210008, China
2. College of Financial Technology, Suzhou Industrial Park Institute of Services Outsourcing, Suzhou 215123, Jiangsu, China
3. College of Smart Agriculture, Suzhou Polytechnic Institute of Agriculture, Suzhou 215008, China

Received:2024-07-21 Online:2025-01-25 Published:2025-02-14

Abstract

Abstract:

In response to the problems of large parameter sizes, high computational complexity, long inference time, and difficulty in deployment on resource-limited mobile devices associated with existing tomato leaf recognition models, this paper proposes a lightweight recognition network called SG-ICA-MobileNetV3. Firstly, a sandglass structure was introduced to modify the inverted residual blocks of MobileNetV3Small, establishing feature transformations and skip connections in high-dimensional space to mitigate information loss and strengthen the model’s feature learning capability. Secondly, an improved coordinate attention mechanism was embedded, integrating global average pooling and max pooling to adaptively learn feature weights at different positions, enhancing the perception ability of diseased areas. Finally, the ReLU activation function was replaced with ELU to alleviate gradient vanishing and the issue of weight bias updated failure during model training, improving network convergence speed. The results showed that the model achieved a classification accuracy of 98.36% on the test set. It outperformed lightweight models such as MobileNetV3Small, MobileNeXt-1.0, MobileVit-S, and ConvNeXt-V2 in terms of parameter count, computational complexity, inference speed, and recognition accuracy, demonstrating strong generalization capabilities. This study result could provide algorithm support for fast and accurate identification of plant leaf diseases.

Key words: sandglass structure, SG-ICA-MobileNetV3, coordinate attention, tomato, leaf, disease recognition

CLC Number:

GU Rui, SONG Cuiling, QIAN Chunhua. A lightweight tomato leaf disease recognition model integrating a sandglass structure with improved coordinate attention[J]. Acta Agriculturae Zhejiangensis, 2025, 37(1): 217-230.

Figures/Tables 14

References 26

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输入 Input	操作 Operator	通道数 Exp_size	输出维度 Out	注意力 ICA	激活函数 Non-Linearity	步长 Stride
224×224×3	Conv2d,3×3	-	16	-	HS	2
112×112×16	SG-ICA bneck,3×3	16	16	√	ELU	2
56×56×16	SG-ICA bneck,3×3	72	24	-	ELU	2
28×28×24	SG-ICA bneck,3×3	88	24	-	ELU	1
28×28×24	SG-ICA bneck,5×5	96	40	√	HS	2
14×14×40	SG-ICA bneck,5×5	240	40	√	HS	1
14×14×40	SG-ICA bneck,5×5	240	40	√	HS	1
14×14×40	SG-ICA bneck,5×5	120	48	√	HS	1
14×14×48	SG-ICA bneck,5×5	144	48	√	HS	1
14×14×48	SG-ICA bneck,5×5	288	96	√	HS	2
7×7×96	SG-ICA bneck,5×5	576	96	√	HS	1
7×7×96	SG-ICA bneck,5×5	576	96	√	HS	1
7×7×96	Conv2d,1×1	-	576	√	HS	1
7×7×576	Pool,1×1	-	-	-	-	1
1×1×576	Conv2d,1×1,NBN	-	-	-	ELU	1
1×1×1024	Conv2d,1×1,NBN	-	-	-	HS	1

输入 Input	操作 Operator	通道数 Exp_size	输出维度 Out	注意力 ICA	激活函数 Non-Linearity	步长 Stride
224×224×3	Conv2d,3×3	-	16	-	HS	2
112×112×16	SG-ICA bneck,3×3	16	16	√	ELU	2
56×56×16	SG-ICA bneck,3×3	72	24	-	ELU	2
28×28×24	SG-ICA bneck,3×3	88	24	-	ELU	1
28×28×24	SG-ICA bneck,5×5	96	40	√	HS	2
14×14×40	SG-ICA bneck,5×5	240	40	√	HS	1
14×14×40	SG-ICA bneck,5×5	240	40	√	HS	1
14×14×40	SG-ICA bneck,5×5	120	48	√	HS	1
14×14×48	SG-ICA bneck,5×5	144	48	√	HS	1
14×14×48	SG-ICA bneck,5×5	288	96	√	HS	2
7×7×96	SG-ICA bneck,5×5	576	96	√	HS	1
7×7×96	SG-ICA bneck,5×5	576	96	√	HS	1
7×7×96	Conv2d,1×1	-	576	√	HS	1
7×7×576	Pool,1×1	-	-	-	-	1
1×1×576	Conv2d,1×1,NBN	-	-	-	ELU	1
1×1×1024	Conv2d,1×1,NBN	-	-	-	HS	1

方案 Schema	基线模型 Base	沙漏结构 SG	改进坐标注意力 ICA	激活函数 ELU	参数量 Params/MB	浮点运算数 FLOPs/10⁶	准确率 Accuracy/%
S0	√				2.40	68.2	93.25
S1	√	√			2.31	67.5	94.36
S2	√		√		2.19	66.6	94.42
S3	√			√	2.34	67.3	94.31
S4	√	√	√		2.28	65.1	96.58
S5	√		√	√	2.19	66.6	95.87
S6		√		√	2.31	67.3	96.53
S7	√	√	√	√	2.28	65.1	98.36

方案 Schema	基线模型 Base	沙漏结构 SG	改进坐标注意力 ICA	激活函数 ELU	参数量 Params/MB	浮点运算数 FLOPs/10⁶	准确率 Accuracy/%
S0	√				2.40	68.2	93.25
S1	√	√			2.31	67.5	94.36
S2	√		√		2.19	66.6	94.42
S3	√			√	2.34	67.3	94.31
S4	√	√	√		2.28	65.1	96.58
S5	√		√	√	2.19	66.6	95.87
S6		√		√	2.31	67.3	96.53
S7	√	√	√	√	2.28	65.1	98.36

类别 Category	准确率 Accuracy	精确率 Precision	召回率 Recall ratio	类别 Category	准确率 Accuracy	精确率 Precision	召回率 Recall ratio
健康叶片Healthy	100.00	100.00	100.00	红蜘蛛病Spider_mite	98.75	98.65	98.21
早疫病Early_blight	97.58	96.96	97.36	晚疫病Late_blight	96.55	96.98	96.06
花叶病Mosaic_virus	98.67	98.81	98.54	褐斑病Target_spot	96.95	96.14	96.26
叶霉病Leaf_mold	97.66	97.03	97.57	斑枯病Septoria_leaf_spot	97.54	97.76	97.11
黄花曲叶病Yellow_leaf_curl	98.66	98.04	97.56	细菌性斑点病Bacterial_spot	99.08	99.22	98.65

A lightweight tomato leaf disease recognition model integrating a sandglass structure with improved coordinate attention

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Figures/Tables 14

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模型 Model	参数量 Params/MB	浮点运算量 FLOPs/10⁶	模型大小 Model size/MB	准确率 Accuracy/%	召回率 Recall ratio/%
ShuffleNetV2^[13]	2.37	302.7	9.65	90.17	91.13
EfficientNet-B2^[14]	4.02	412.3	15.37	91.55	92.28
MobileNetV2-1.0^[23]	3.50	314.1	16.78	92.46	93.36
MobileNeXt-1.0^[20]	3.42	298.7	16.45	93.51	93.97
MobileNetV3Small^[24]	2.40	68.2	4.85	93.73	93.25
MobileVit-S^[25]	5.62	700.4	18.43	93.91	94.53
ConvNeXt-V2^[26]	5.21	380.6	21.26	94.29	94.84
SG-ICA-MobileNetV3	2.28	65.1	4.36	98.36	97.99

类别 Category	准确率 Accuracy/%	精确率 Precision/%	召回率 Recall ratio/%	平均推理时间 Average inference time/ms
斑点落叶病Alternaria_boltch	97.25	97.12	96.58	42.7
灰斑病Grey_spot	97.25	96.23	97.44	43.5
锈病Rust	96.32	96.37	96.26	44.6
褐斑病Brown_spot	98.14	98.27	98.45	41.4
白粉病Powdery_mildew	97.86	98.18	98.24	43.6
健康叶片Healthy	99.77	99.39	99.01	39.7
平均Average	97.76	97.59	97.66	42.58

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