Plant disease identification based on pruning

doi:10.3969/j.issn.1004-1524.2023.06.23

Acta Agriculturae Zhejiangensis ›› 2023, Vol. 35 ›› Issue (6): 1462-1472.DOI: 10.3969/j.issn.1004-1524.2023.06.23

• Biosystems Engineering • Previous Articles Next Articles

Plant disease identification based on pruning

ZHU Dongqin¹(), FENG Quan¹^,^*(), ZHANG Jianhua²

1. School of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
2. Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Received:2022-07-04 Online:2023-06-25 Published:2023-07-04

Abstract

Abstract:

In order to automatically detect plant diseases in real time, disease identification model need to be deployed on edge/mobile devices. However, the deep convolutional neural networks with superior performance in the field of disease identification cannot be directly deployed due to the limitation of model size and computing resources. In order to solve this problem, a disease identification method based on pruning was proposed, which used the γ coefficient in the BN layer to perform channel pruning to achieve the compression of Vgg16, ResNet164 and DenseNet40 networks. Taking the PlantVillage dataset as the research object, the 3 networks were compressed. The experimental results showed that the average accuracy of the compressed Vgg16-80%、ResNet164-80% and DenseNet40-80% were 97.46%, 99.12% and 99.68%, respectively, and DenseNet40-80% had the highest accuracy and the least amount of parameters, only 0.27×10⁶. Vgg16-80% had the most obvious compression effect, pruned 97.83% of the parameters and 96.77% of the computation. The computation of the pruned Vgg16-80% were the smallest, only 0.01×10⁹. The accuracy of the pruned Vgg16-80% and DenseNet40-80% were higher than the original model. Therefore, this method could solve the problem of over-parameterization of large-scale neural networks, reduce computing costs, and provide ideas for the deployment of existing large-scale networks on small devices.

Key words: convolutional neural network, disease identification, pruning, model compression

CLC Number:

ZHU Dongqin, FENG Quan, ZHANG Jianhua. Plant disease identification based on pruning[J]. Acta Agriculturae Zhejiangensis, 2023, 35(6): 1462-1472.

Figures/Tables 11

References 29

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模型 Model	准确率 Accuracy/%	参数量 Parameter/10⁶	删减比例 Pruned/%	浮点运算数 FLOPs/10⁹	删减比例 Pruned/%	模型尺寸 Model size/MB	删减比例 Pruned/%
Vgg16(Normal)	96.76	14.74	—	0.31	—	118	—
Vgg16(Sparse)	96.60	14.74	—	0.31	—	118	—
Vgg16-70%	98.19	0.90	93.89	0.03	90.32	7.3	93.81
Vgg16-80%	97.46	0.32	97.83	0.01	96.77	2.6	97.80
ResNet164(Normal)	99.55	1.72	—	0.26	—	14.1	—
ResNet164(Sparse)	98.84	1.72	—	0.26	—	14.1	—
ResNet164-70%	99.28	0.51	70.35	0.07	73.08	4.4	68.79
ResNet164-80%	99.12	0.37	78.49	0.05	80.77	3.3	76.60
DenseNet40(Normal)	99.62	1.08	—	0.29	—	8.8	—
DenseNet40(Sparse)	99.66	1.08	—	0.29	—	8.8	—
DenseNet40-70%	99.64	0.37	65.74	0.13	55.17	3.1	64.77
DenseNet40-80%	99.68	0.27	75.00	0.10	65.52	2.3	73.86
DenseNet40-90%	99.51	0.16	85.19	0.06	79.31	1.4	84.09

模型 Model	准确率 Accuracy/%	参数量 Parameter/10⁶	删减比例 Pruned/%	浮点运算数 FLOPs/10⁹	删减比例 Pruned/%	模型尺寸 Model size/MB	删减比例 Pruned/%
Vgg16(Normal)	96.76	14.74	—	0.31	—	118	—
Vgg16(Sparse)	96.60	14.74	—	0.31	—	118	—
Vgg16-70%	98.19	0.90	93.89	0.03	90.32	7.3	93.81
Vgg16-80%	97.46	0.32	97.83	0.01	96.77	2.6	97.80
ResNet164(Normal)	99.55	1.72	—	0.26	—	14.1	—
ResNet164(Sparse)	98.84	1.72	—	0.26	—	14.1	—
ResNet164-70%	99.28	0.51	70.35	0.07	73.08	4.4	68.79
ResNet164-80%	99.12	0.37	78.49	0.05	80.77	3.3	76.60
DenseNet40(Normal)	99.62	1.08	—	0.29	—	8.8	—
DenseNet40(Sparse)	99.66	1.08	—	0.29	—	8.8	—
DenseNet40-70%	99.64	0.37	65.74	0.13	55.17	3.1	64.77
DenseNet40-80%	99.68	0.27	75.00	0.10	65.52	2.3	73.86
DenseNet40-90%	99.51	0.16	85.19	0.06	79.31	1.4	84.09

模型 Model	准确率 Accuracy/ %	参数量 Parameter/ 10⁶	浮点运算数 FLOPs/10⁹	模型尺寸 Model size/MB
Vgg16-80%	97.46	0.32	0.010	2.6
ResNet164-80%	99.12	0.37	0.050	3.3
DenseNet40-80%	99.68	0.27	0.100	2.3
MobileNetV2	97.40	2.27	0.006	18.4
EfficientnetV2-S	97.12	20.22	0.060	162.7
ShuffleNetV2	97.98	1.29	0.003	10.5

模型 Model	准确率 Accuracy/ %	参数量 Parameter/ 10⁶	浮点运算数 FLOPs/10⁹	模型尺寸 Model size/MB
Vgg16-80%	97.46	0.32	0.010	2.6
ResNet164-80%	99.12	0.37	0.050	3.3
DenseNet40-80%	99.68	0.27	0.100	2.3
MobileNetV2	97.40	2.27	0.006	18.4
EfficientnetV2-S	97.12	20.22	0.060	162.7
ShuffleNetV2	97.98	1.29	0.003	10.5

基础模型 Basic model	分类数量 Category number	存储空间 Storage space/MB	平均准确率 Average accuracy/%
AlexNet^[3]	38	2.60	99.56
MobileNet V1^[9]	38	17.1	95.02
Inception V3^[9]	38	87.5	95.62
SqueezeNet^[10]	38	0.62	98.13
DenseNet40-90%	38	0.64	99.51

Plant disease identification based on pruning

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