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

YIN Xianming, JI Yu, ZHANG Riqing, MO Dengkui, PENG Shaofeng, WEI Wei

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (3) : 29-36.

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南京林业大学学报（自然科学版）
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JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (3) : 29-36. DOI: 10.12302/j.issn.1000-2006.202112037

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

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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.

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deep learning / Camellia oleifera leaf / variety recognition / GoogLeNet / ResNet

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YIN Xianming , JI Yu , ZHANG Riqing , et al . Research on recognition of Camellia oleifera leaf varieties based on deep learning[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2023, 47(3): 29-36 https://doi.org/10.12302/j.issn.1000-2006.202112037

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis
[1]
王金凤, 谭新建, 吴喜昌, 等. 我国油茶产业发展现状与对策建议[J]. 世界林业研究, 2020, 33(6): 80-85.
WANG J F, TAN X J, WU X C, et al. Development status and suggestions of Camellia industry in China[J]. World For Res, 2020, 33(6): 80-85. DOI: 10.13348/j.cnki.sjlyyj.2020.0103.y.
Cited in this article [2]
[2]
李志钢, 马力, 陈永忠, 等. 我国油茶籽的综合利用现状概述[J]. 绿色科技, 2018(6): 191-194.
LI Z G, MA L, CHEN Y Z, et al. Study on the comprehensive utilization of oil-tea Camellia seed[J]. J Green Sci Technol, 2018(6): 191-194. DOI: 10.16663/j.cnki.lskj.2018.06.068.
Cited in this article [1]
[3]
南玉龙, 张慧春, 郑加强, 等. 深度学习在林业中的应用[J]. 世界林业研究, 2021, 34(5): 87-90.
NAN Y L, ZHANG H C, ZHENG J Q, et al. Application of deep learning to forestry[J]. World For Res, 2021, 34(5): 87-90. DOI: 10.13348/j.cnki.sjlyyj.2021.0020.y.
Cited in this article [1]
[4]
阳灵燕, 张红燕, 陈玉峰, 等. 机器学习在农作物品种识别中的应用研究进展[J]. 中国农学通报, 2020, 36(30): 158-164.
YANG L Y, ZHANG H Y, CHEN Y F, et al. The application of machine learning in crop variety recognition: a review[J]. Chin Agric Sci Bull, 2020, 36(30): 158-164. DOI: 10.11924/j.issn.1000-6850.casb20191100813.
Cited in this article [1]
[5]
周惠汝, 吴波明. 深度学习在作物病害图像识别方面应用的研究进展[J]. 中国农业科技导报, 2021, 23(5): 61-68.
ZHOU H R, WU B M. Advances in research on deep learning for crop disease image recognition[J]. J Agric Sci Technol, 2021, 23(5): 61-68. DOI: 10.13304/j.nykjdb.2019.1041.
Cited in this article [1]
[6]
严恩萍, 棘玉, 尹显明, 等. 基于无人机影像自动检测冠层果的油茶快速估产方法[J]. 农业工程学报, 2021, 37(16): 39-46.
YAN E P, JI Y, YIN X M, et al. Rapid estimation of Camellia oleifera yield based on automatic detection of canopy fruits using UAV images[J]. Trans Chin Soc Agric Eng, 2021, 37(16): 39-46. DOI: 10.11975/j.issn.1002-6819.2021.16.006.
Cited in this article [1]
[7]
余凯, 贾磊, 陈雨强, 等. 深度学习的昨天、今天和明天[J]. 计算机研究与发展, 2013, 50(9): 1799-1804.
YU K, JIA L, CHEN Y Q, et al. Deep learning: yesterday, today, and tomorrow[J]. J Comput Res Dev, 2013, 50(9): 1799-1804.
Cited in this article [1]
[8]
KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Commun ACM, 2017, 60(6): 84-90. DOI: 10.1145/3065386.
Cited in this article [1]
[9]
ZEILER M D, FERGUS R. Visualizing andunderstanding convolutional networks[C]// European Conference on Computer Vision. Cham: Springer, 2014: 818-833. DOI: 10.1007/978-3-319-10590-1_53.
Cited in this article [1]
[10]
SIMONYAN K, ZISSERMAN A. Verydeep convolutional networks for large-scale image recognition[C]// International Conference on Learning Representations (ICLR). 2015: 1-14. arXiv preprint arXiv:14091556, 2014.
Cited in this article [1]
[11]
HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). June 27-30, 2016, Las Vegas, NV, USA. IEEE, 2016:770-778. DOI: 10.1109/CVPR.2016.90.
Cited in this article [2]
[12]
SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 7-12, 2015, Boston, MA, USA. IEEE, 2015: 1-9. DOI: 10.1109/CVPR.2015.7298594.
Cited in this article [2]
[13]
韩斌, 曾松伟. 基于多特征融合和卷积神经网络的植物叶片识别[J]. 计算机科学, 2021, 48(S1): 113-117.
HAN B, ZENG S W. Plant leaf image recognition based on multi-feature integration and convolutional neural network[J]. Comput Sci, 2021, 48(S1): 113-117. DOI: 10.11896/jsjkx.201100119.
Cited in this article [1]
[14]
原忠虎, 王维, 苏宝玲. 基于改进VGGNet模型的外来入侵植物叶片识别方法[J]. 计算机与现代化, 2021(9): 7-11.
YUAN Z H, WANG W, SU B L. Leaf recognition method of invasive alien plants based on improved VGGNet model[J]. Computer and Modernization, 2021(9): 7-11.
Cited in this article [1]
[15]
王建霞, 张成, 闫双双. 基于卷积神经网络的宠物猫品种分类研究[J]. 河北工业科技, 2020, 37(6): 407-412.
WANG J X, ZHANG C, YAN S S. Research on pet cat breed classification based on convolutional neural network[J]. Hebei J Ind Sci Technol, 2020, 37(6): 407-412. DOI: 10.7535/hbgykj.2020yx06004.
Cited in this article [1]
[16]
石洪康, 田涯涯, 杨创, 等. 基于卷积神经网络的家蚕幼虫品种智能识别研究[J]. 西南大学学报(自然科学版), 2020, 42(12): 34-45.
SHI H K, TIAN Y Y, YANG C, et al. Research on intelligent recognition of silkworm larvae races based on convolutional neural networks[J]. J Southwest Univ (Nat Sci Ed), 2020, 42(12): 34-45. DOI: 10.13718/j.cnki.xdzk.2020.12.004.
Cited in this article [2]
[17]
HOWARD A G, ZHU M L, CHEN B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[EB/OL]. 2017: arXiv: 1704.04861. https://arxiv.org/abs/1704.04861.
https://arxiv.org/abs/1704.04861
Cited in this article [1]
[18]
杨静亚, 李景霞, 王振宇, 等. 基于卷积神经网络的花朵品种的识别[J]. 黑龙江大学工程学报, 2019, 10(4): 90-96.
YANG J Y, LI J X, WANG Z Y, et al. Identification of flower varieties based on convolution neural network[J]. J Eng of Heilongjiang Univ, 2019, 10(4): 90-96. DOI: 10.13524/j.2095-008x.2019.04.060.
Cited in this article [1]
[19]
游嘉伟, 王斌, 曾瑞. 用于大豆品种识别的叶片深度特征学习方法[J]. 计算机系统应用, 2021, 30(10): 118-127.
YOU J W, WANG B, ZENG R. Learning method of leaf deep features for soybean cultivar recognition[J]. Computer Systems and Applications, 2021, 30(10): 118-127.
Cited in this article [1]
[20]
薄琪苇. 基于卷积神经网络的植物叶片识别研究[D]. 杭州: 浙江农林大学, 2018.
BO Q W. Study on plant leaf recognition based on convolutional neural network[D]. Hangzhou: Zhejiang A&F University, 2018.
Cited in this article [1]
[21]
孙洋. 基于Android平台的植物叶片识别系统[D]. 保定: 河北大学, 2017.
SUN Y. Plant leaf recognition system based on android platform[D]. Baoding: Hebei University, 2017.
Cited in this article [1]
[22]
ABADI M, BARHAM P, CHEN J, et al. TensorFlow: a system for large-scale machine learning[C]// USENIX Association. Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation. Savannah, GA, USA, 2016: 265-283. DOI: 10.5555/3026877.3026899.
Cited in this article [1]
[23]
XIN J, ZHANG Y X, TANG Y, et al. Brain differences between men and women: evidence from deep learning[J]. Front Neurosci, 2019, 13: 185. DOI: 10.3389/fnins.2019.00185.
Cited in this article [1]
[24]
IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]// Proceedings of the 32nd International Conference on International Conference on Machine Learning-Volume 37. JMLR.org. Lille, France, 2015: 448-456.
Cited in this article [1]
[25]
MANNING C D, RAGHAVAN P, SCHUTZE H. Introduction to information retrieval[M]. Cambridge: Cambridge University Press, 2008. DOI: 10.1017/CBO9780511809071.
Cited in this article [1]
[26]
SOKOLOVA M, LAPALME G. A systematic analysis of performance measures for classification tasks[J]. Inf Process Manag, 2009, 45(4): 427-437. DOI: 10.1016/j.ipm.2009.03.002.
Cited in this article [1]
[27]
朱晓龙. 基于深度卷积神经网络的树木生境叶片识别方法研究[D]. 哈尔滨: 东北林业大学, 2020.
ZHU X L. Study of tree leaf recognition in habitat based on deep convolutional neural networks[D]. Harbin: Northeast Forestry University, 2020.
Cited in this article [1]
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