Identification and application of walnut disease based on CA-MobileNet-V2

doi:10.3969/j.issn.1004-1524.20221763

Acta Agriculturae Zhejiangensis ›› 2023, Vol. 35 ›› Issue (12): 2977-2987.DOI: 10.3969/j.issn.1004-1524.20221763

• Biosystems Engineering • Previous Articles Next Articles

Identification and application of walnut disease based on CA-MobileNet-V2

LI Rongpeng(), MAMAT Sawut(), SHENG Yanfang, HE Xugang

College of Geography and Remote Sensing Sciences; b. Xinjiang Key Laboratory of Oasis Ecology; c. Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830017, China

Received:2022-12-12 Online:2023-12-25 Published:2023-12-27

Abstract

Abstract:

Disease invasion is one of the important factors restricting the high-quality development of walnut. In order to realize intelligent disease identification in the field, this study designed a walnut disease identification model. The model used Mobilenet-V2 as the basic network skeleton, and added a coordinate attention mechanism to the inverted residual structure to make up for the lack of location information during feature extraction. In addition, this study designed a mixed transfer training method, which combined cross-domain and intra-domain to avoid the adverse effects of separate transfer learning. The results showed that: 1) The mixed transfer had the best effect on improving the model, and the highest accuracy rate was increased by 18.57 percentage points. 2) The average identification accuracy of the model was 96.97%, the model parameter size was 3.95 M, and the memory occupancy was 10.50 MB. Compared with Mobilenet-V3-Large, ShuffulNet-V2 and EfficientNet-V2-S, the identification accuracy was increased by 4.39, 6.63 and 4.31 percentage points, respectively, and the parameter size and memory occupation were keep small. 3) Compared with SE (squeeze-and-excitation) and CBAM (convolutional block attention module), the coordinate attention mechanism could improve the model’s attention to the region of interest. Therefore, the model could be used to develop an Android application and deploy it on the mobile terminal, and provide a new method for intelligent identification of walnut disease.

Key words: walnut disease, coordinate attention mechanism, mixed transfer, Android application

CLC Number:

S436.6

LI Rongpeng, MAMAT Sawut, SHENG Yanfang, HE Xugang. Identification and application of walnut disease based on CA-MobileNet-V2[J]. Acta Agriculturae Zhejiangensis, 2023, 35(12): 2977-2987.

Figures/Tables 9

References 21

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病害类型 Disease category	标签 Labels	样本数量Number of samples
		增强前Before augmentation		增强后After augmentation
		数据集A Dataset A	数据集B Dataset B	数据集A Dataset A	数据集B Dataset B
虫蛀叶Insect pest	0	152	261	679	1 139
健康叶Healthy	1	123	145	616	1 012
褐斑病Brown spot	2	105	125	630	1 000
黑斑病Black spot	3	98	189	686	1 134
缺素病Element deficiency	4	162	242	656	1 210
炭疽病Anthrax	5	116	168	696	1 008
总计Total		756	1 130	3 963	6 503

病害类型 Disease category	标签 Labels	样本数量Number of samples
		增强前Before augmentation		增强后After augmentation
		数据集A Dataset A	数据集B Dataset B	数据集A Dataset A	数据集B Dataset B
虫蛀叶Insect pest	0	152	261	679	1 139
健康叶Healthy	1	123	145	616	1 012
褐斑病Brown spot	2	105	125	630	1 000
黑斑病Black spot	3	98	189	686	1 134
缺素病Element deficiency	4	162	242	656	1 210
炭疽病Anthrax	5	116	168	696	1 008
总计Total		756	1 130	3 963	6 503

迁移方式 Transfer methods	数据集 Dataset	准确率 Accuracy/ %
无迁移 No transfer	数据集B Dataset B	86.62
learning
跨域迁移 Cross-domain	ImageNet数据集+数据集B Datase ImageNet+Dataset B	93.53
transfer
域内迁移 Intra-domain	数据集A+数据集B Dataset A+Dataset B	78.40
transfer
混合迁移 Mixed transfer	ImageNet数据集+数据集A+数据集B Dataset ImageNet+Dataset A+Dataset B	96.97

迁移方式 Transfer methods	数据集 Dataset	准确率 Accuracy/ %
无迁移 No transfer	数据集B Dataset B	86.62
learning
跨域迁移 Cross-domain	ImageNet数据集+数据集B Datase ImageNet+Dataset B	93.53
transfer
域内迁移 Intra-domain	数据集A+数据集B Dataset A+Dataset B	78.40
transfer
混合迁移 Mixed transfer	ImageNet数据集+数据集A+数据集B Dataset ImageNet+Dataset A+Dataset B	96.97

模型 Models	类别 Classification	精确率 Precision/%	召回率 Recall/%	F1值 F1score/%	平均准确率 Mean accuracy%	参数量 Params/M	内存占有 Memory/MB
VGG16	虫害Insect pest	87.27	63.44	73.47	64.48	138.23	512.27
	健康Healthy	79.12	35.64	49.15
	褐斑Brown spot	51.30	49.50	50.38
	黑斑Black spot	49.54	94.25	64.94
	缺素Element deficiency	78.73	71.90	75.16
	炭疽Anthrax	68.18	67.16	67.66
ResNet18	虫害Insect pest	95.63	96.48	96.05	97.31	11.82	42.72
	健康Healthy	99.50	100.00	99.75
	褐斑Brown spot	94.17	96.04	95.10
	黑斑Black spot	99.06	94.20	96.57
	缺素Element deficiency	98.00	99.49	98.74
	炭疽Anthrax	98.02	98.51	98.26
Mobilenet-V3-Large	虫害Insect pest	87.34	92.17	89.69	92.58	5.42	21.65
	健康Healthy	96.55	96.55	96.55
	褐斑Brown spot	86.41	89.48	87.92
	黑斑Black spot	92.96	85.71	89.19
	缺素Element deficiency	93.00	93.00	93.00
	炭疽Anthrax	95.05	94.12	94.58
ShuffulNet-V2	虫害Insect pest	83.84	91.87	87.67	90.34	2.28	9.03
	健康Healthy	92.12	92.57	92.35
	褐斑Brown spot	85.92	85.55	85.71
	黑斑Black spot	92.96	85.71	89.19
	缺素Element deficiency	93.00	93.00	93.00
	炭疽Anthrax	95.05	94.12	97.57
EfficientNet-V2-S	虫害Insect pest	87.77	93.50	90.54	92.66	23.25	81.28
	健康Healthy	95.07	94.61	94.84
	褐斑Brown spot	88.35	90.55	89.44
	黑斑Black spot	94.67	88.55	91.36
	缺素Element deficiency	95.00	93.60	94.29
	炭疽Anthrax	96.04	95.57	95.80
CA-Mobilenet-V2	虫害Insect pest	95.20	96.04	95.61	96.97	3.95	10.50
	健康Healthy	99.02	99.51	99.26
	褐斑Brown spot	93.20	96.00	94.58
	黑斑Black spot	98.59	92.92	95.67
	缺素Element deficiency	98.00	99.49	98.74
	炭疽Anthrax	98.02	98.51	98.26

Identification and application of walnut disease based on CA-MobileNet-V2

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模型 Model	准确率 Accuracy/ %	漏检率 Missing/ %	误检率 Error/%	耗时 Time consuming/s
CA-MobileNet-V2	90.83	4.17	5.00	0.53
ResNet18	92.50	4.17	3.33	3.37
MobileNet-V3-Large	87.50	5.83	6.67	1.05
ShuffulNet-V2	82.50	5.00	12.50	0.36
EfficientNet-V2-S	86.67	7.50	5.83	5.09

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