Research on recognition of Camellia oleifera leaf varieties based on deep learning

doi:10.12302/j.issn.1000-2006.202112037

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (3): 29-36.doi: 10.12302/j.issn.1000-2006.202112037

Special Issue: 第三届中国林草计算机应用大会论文精选

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Research on recognition of Camellia oleifera leaf varieties based on deep learning

YIN Xianming¹(), JI Yu¹, ZHANG Riqing¹^,^*(), MO Dengkui¹, PENG Shaofeng², WEI Wei³

1. College of Forestry, Central South University of Forestry and Technology, Changsha 410004, China
2. Hunan Academy of Forestry, Changsha 410004, China
3. Guangxi Forestry Research Institute, Nanning 530002, China

Received:2021-12-23 Revised:2022-04-19 Online:2023-05-30 Published:2023-05-25
Contact: ZHANG Riqing E-mail:784428411@qq.com;hanszrq@sina.com

Abstract

Abstract:

【Objective】Deep learning methods are used to carry out research on Camellia oleifera based variety recognition on leaves, this study developed C. oleifera strain image recognition technology to provide scientific basis for C. oleifera variety identification.【Method】Eleven leaves of C. oleifera varieties grown under natural lighting conditions and free from pests and diseases were collected for a study. Images of the front and back of the leaves with a white cardboard background were captured using a smartphone. Invalid images were removed by usability screening, and a dataset of camellia leaf varieties with 2 791 images was constructed. Deep learning networks (GoogLeNet and ResNet) were used to identify and study the leaf images of 11 C. oleifera varieties.【Result】Both GoogLeNet and ResNet networks can meet the requirements of C. oleifera variety recognition based on leaves, with overall F₁ scores of 94.0% and 80.7%. Among them, the GoogLeNet network was more effective in recognition, with average accuracy, recall, Macro F₁ and Micro F₁ value of 94.1%, 94.0%, 94.0% and 96.9%, respectively, and its recognition recall for two varieties, NO. 1 and 8, reached 100%.【Conclution】Deep learning networks (GoogLeNet and ResNet) can achieve C. oleifera variety recognition based on leaves, which can provide a reference for rapid leaf-based C. oleifera variety recognition.

Key words: deep learning, Camellia oleifera leaf, variety recognition, GoogLeNet, ResNet

CLC Number:

TP391.41

YIN Xianming, JI Yu, ZHANG Riqing, MO Dengkui, PENG Shaofeng, WEI Wei. Research on recognition of Camellia oleifera leaf varieties based on deep learning[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2023, 47(3): 29-36.

Figures/Tables 9

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Research on recognition of Camellia oleifera leaf varieties based on deep learning

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品种编号 variety No.	总数量 total quantity	训练集 training set	验证集 validation set	测试集 test set
1	158	94	32	32
2	261	156	53	52
3	283	169	57	57
4	270	162	54	54
5	230	138	46	46
6	309	185	62	62
7	211	126	43	42
8	301	180	61	60
9	306	183	62	61
10	214	128	43	43
11	248	148	50	50

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品种编号 variety No.	总数量 total quantity	训练集 training set	验证集 validation set	测试集 test set
1	158	94	32	32
2	261	156	53	52
3	283	169	57	57
4	270	162	54	54
5	230	138	46	46
6	309	185	62	62
7	211	126	43	42
8	301	180	61	60
9	306	183	62	61
10	214	128	43	43
11	248	148	50	50

品种编号 variety No.	总数量 total quantity	训练集 training set	验证集 validation set	测试集 test set
1	158	94	32	32
2	261	156	53	52
3	283	169	57	57
4	270	162	54	54
5	230	138	46	46
6	309	185	62	62
7	211	126	43	42
8	301	180	61	60
9	306	183	62	61
10	214	128	43	43
11	248	148	50	50