Plant leaf disease identification based on contrastive learning

doi:10.3969/j.issn.1004-1524.20230093

Acta Agriculturae Zhejiangensis ›› 2024, Vol. 36 ›› Issue (1): 215-224.DOI: 10.3969/j.issn.1004-1524.20230093

• Biosystems Engineering • Previous Articles Next Articles

Plant leaf disease identification based on contrastive learning

YANG Xinyu¹(), FENG Quan¹^,^*(), ZHANG Jianhua², YANG Sen¹

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

Received:2023-01-30 Online:2024-01-25 Published:2024-02-18

Abstract

Abstract:

At present, the recognition of plant disease via image processing mostly relies on the manually labeled convolutional neural network. However, the self-monitoring contrastive learning could achieve independent learning without relying on labels and large amounts of data. In view of this advantage, the effect of four contrastive learning methods, MoCo-v2, DeepCluster-v2, SwAV and BYOL, on the identification of plant leaf diseases were compared by setting different experimental conditions on the open-source dataset of PlantVillage and the self-built cotton disease dataset. The ResNet50 encoder trained by four contrastive learning methods was tested for disease identification both under Linear and Finetune modes, and the feasibility of the contrastive learning methods in identifying plant leaf diseases was evaluated. It was shown that the average accuracy under Finetune mode on the PlantVillage dataset was higher than that under Linear mode, and the highest identificaiton accuracy of the encoders trained by the four methods reached 99.83%. DeepCluster-v2 and BYOL had the highest identification rate under Finetune mode, both of which were 99.87%. On the self-built cotton disease dataset, the performance under Finetune mode was poorer than that under Linear mode, and the highest idenficaiton accuracy of DeepCluster-v2 under Linear mode was 98.86%. Overall, the disease identification rate based on contrastive learning method was superior to the supervised models, demonstrating good application prospects in plant leaf disease identification.

Key words: contrastive learning, disease identification, image processing

CLC Number:

TP391.41

YANG Xinyu, FENG Quan, ZHANG Jianhua, YANG Sen. Plant leaf disease identification based on contrastive learning[J]. Acta Agriculturae Zhejiangensis, 2024, 36(1): 215-224.

Figures/Tables 8

References 19

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模式 Mode	Beach_size	优化方法 Optimization method	各对比学习方法的准确率Accuracy of contrastive learning methods
模式 Mode	Beach_size	优化方法 Optimization method	MoCo-v2	DeepCluster-v2	SwAV	BYOL	平均值Mean
Linear	32	SGD+momentum	97.26	99.70	99.74	99.70	99.10
Linear	64	SGD+momentum	96.92	99.79	99.78	99.69	99.04
Finetune	32	SGD+momentum	99.13	99.75	99.71	99.66	99.56
Finetune	64	SGD+momentum	99.70	98.57	99.86	99.87	99.50
Finetune	32	SGD+momentum+Adam	99.52	99.77	99.64	99.59	99.63
Finetune	64	SGD+momentum+Adam	99.75	99.87	99.86	99.84	99.83

模式 Mode	Beach_size	优化方法 Optimization method	各对比学习方法的准确率Accuracy of contrastive learning methods
模式 Mode	Beach_size	优化方法 Optimization method	MoCo-v2	DeepCluster-v2	SwAV	BYOL	平均值Mean
Linear	32	SGD+momentum	97.26	99.70	99.74	99.70	99.10
Linear	64	SGD+momentum	96.92	99.79	99.78	99.69	99.04
Finetune	32	SGD+momentum	99.13	99.75	99.71	99.66	99.56
Finetune	64	SGD+momentum	99.70	98.57	99.86	99.87	99.50
Finetune	32	SGD+momentum+Adam	99.52	99.77	99.64	99.59	99.63
Finetune	64	SGD+momentum+Adam	99.75	99.87	99.86	99.84	99.83

类别 Category	准确率 Accuracy	类别 Category	准确率 Accuracy	类别 Category	准确率 Accuracy	类别 Category	准确率 Accuracy
C1	100.0	C11	99.54	C21	100.0	C31	99.73
C2	100.0	C12	100.0	C22	100.0	C32	99.50
C3	100.0	C13	100.0	C23	100.0	C33	99.12
C4	100.0	C14	100.0	C24	100.0	C34	99.68
C5	100.0	C15	100.0	C25	100.0	C35	99.68
C6	100.0	C16	100.0	C26	100.0	C36	99.91
C7	100.0	C17	100.0	C27	100.0	C37	100.0
C8	94.96	C18	100.0	C28	100.0	C38	100.0
C9	100.0	C19	100.0	C29	100.0
C10	96.28	C20	100.0	C30	98.98

类别 Category	准确率 Accuracy	类别 Category	准确率 Accuracy	类别 Category	准确率 Accuracy	类别 Category	准确率 Accuracy
C1	100.0	C11	99.54	C21	100.0	C31	99.73
C2	100.0	C12	100.0	C22	100.0	C32	99.50
C3	100.0	C13	100.0	C23	100.0	C33	99.12
C4	100.0	C14	100.0	C24	100.0	C34	99.68
C5	100.0	C15	100.0	C25	100.0	C35	99.68
C6	100.0	C16	100.0	C26	100.0	C36	99.91
C7	100.0	C17	100.0	C27	100.0	C37	100.0
C8	94.96	C18	100.0	C28	100.0	C38	100.0
C9	100.0	C19	100.0	C29	100.0
C10	96.28	C20	100.0	C30	98.98

模式 Mode	Beach_size	各对比学习方法的准确率Accuracy of contrastive learning methods
模式 Mode	Beach_size	MoCo-v2	DeepCluster-v2	SwAV	BYOL	平均值Mean
Linear	32	97.29	98.29	97.20	98.30	97.77
Linear	64	98.36	98.86	98.47	98.56	98.56
Finetune	32	96.38	96.56	96.38	96.23	96.39
Finetune	64	97.11	97.47	97.47	96.93	97.35

Plant leaf disease identification based on contrastive learning

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类别 Category	精确率Precision				召回率Recall				F1值F1 score
类别 Category	M1	M2	M3	M4	M1	M2	M3	M4	M1	M2	M3	M4
D1	97.10	98.33	97.21	97.63	96.40	98.01	96.96	97.2	96.75	98.17	97.08	97.41
D2	97.85	97.17	97.81	97.44	99.21	98.10	99.01	98.7	98.53	97.63	98.41	98.07
D3	99.65	99.78	99.63	99.59	98.80	99.12	98.70	98.79	99.22	99.45	99.16	99.19
D4	99.59	99.60	99.67	99.64	100.00	100.00	100.00	100.00	99.79	99.80	99.83	99.82
D5	99.03	99.40	99.17	99.21	99.67	99.85	99.76	99.87	99.35	99.62	99.46	99.54
D6	87.78	89.66	88.00	87.79	86.22	86.67	85.56	85.19	86.99	88.14	86.76	86.47
D7	98.99	98.50	98.87	98.71	98.74	98.13	98.59	98.39	98.86	98.31	98.73	98.55
D8	99.83	99.67	99.79	99.75	99.65	100.00	99.79	99.75	99.74	99.83	99.79	99.75

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