Recognition of Huanglongbing symptom based on deep convolutional neural network

DAI Zehan; ZHENG Zheng; HUANG Lishu; LAI Yunyan; BAO Minli; XU Meirong; DENG Xiaoling

doi:10.7671/j.issn.1001-411X.201909031

Journal of South China Agricultural University > 2020 > 41(4): 111-119. > DOI: 10.7671/j.issn.1001-411X.201909031

DAI Zehan, ZHENG Zheng, HUANG Lishu, et al. Recognition of Huanglongbing symptom based on deep convolutional neural network[J]. Journal of South China Agricultural University, 2020, 41(4): 111-119. DOI: 10.7671/j.issn.1001-411X.201909031

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Recognition of Huanglongbing symptom based on deep convolutional neural network

College of Agriculture, South China Agricultural University, Guangzhou 510642, China

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Received Date: September 16, 2019
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
To explore the capability of deploying deep learning to the detection of Huanglongbing (HLB) symptom inCitrus spp., and evaluate the classification accuracies of the classifiers.

Method
Two-class classifiers(I-2-C and M-2-C) and eight-class classifiers(I-8-C and M-8-C) were constructed using images of diseased leaves caused by HLB/non-HLB and healthy leaves based on convolutional neural networks and transfer learning.

Result
The overall classification performance of M-8-C stood out in all classifiers with accuracy of 93.7%, implying great capability in deep convolutional neural networks for classifying HLB symptoms. The mean F1 socres of I-8-C and M-8-C were 77.9% and 88.4% respectively, which were higher than those of I-2-C(56.3%) and M-2-C(52.5%). This indicated that subtyping symptoms could help improve the recognition ability of models. The slightly higher mean F1 score of M-8-C compared with I-8-C indicated that the eight-class model based on MobileNetV1 had better performance than the one based on InceptionV3. An optimized model, namely M-8f-C, was developed based on M-8-C and was successfully mounted on mobile phone. The field tests showed that M-8f-C was of decent performance under field conditions.

Conclusion
Classifier based on deep learning and transfer learning has high accuracy for recognizing HLB symptom leaves.
- Citrus Huanglongbing,
- symptom recognition,
- convolutional neural network,
- transfer learning

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References (26)

References

[1]	林孔湘. 柑桔黄梢(黄龙)病研究Ⅰ: 病情调查[J]. 植物病理学报, 1956, 2(1): 1-11.
[2]	DENG X, LAN Y, HONG T, et al. Citrus greening detection using visible spectrum imaging and C-SVC[J]. Comput Electron Agr, 2016, 130(2016): 177-183.
[3]	GARCIA-RUIZ F, SANKARAN S, MAJA J M, et al. Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees[J]. Comput Electron Agr, 2013, 91: 106-115. doi: 10.1016/j.compag.2012.12.002
[4]	MISHRA A, KARIMI D, EHSANI R, et al. Evaluation of an active optical sensor for detection of Huanglongbing (HLB) disease[J]. Biosyst Eng, 2011, 110(3): 302-309. doi: 10.1016/j.biosystemseng.2011.09.003
[5]	MISHRA A, KARIMI D, EHSANI R, et al. Identification of citrus greening (Hlb) using a vis-nir spectroscopy technique[J]. T ASABE, 2012, 55(2): 711-720. doi: 10.13031/2013.41369
[6]	SANKARAN S, MISHRA A, MAJA J M, et al. Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards[J]. Comput Electron Agr, 2011, 77(2): 127-134. doi: 10.1016/j.compag.2011.03.004
[7]	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
[8]	ZHANG Y C, KAGEN A C. Machine learning interface for medical image analysis[J]. J Digit Imaging, 2017, 30(5): 615-621. doi: 10.1007/s10278-016-9910-0
[9]	ESTEVA A, KUPREL B, NOVOA R A, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542(7639): 115-118. doi: 10.1038/nature21056
[10]	LEE H, KIM K G. Applying deep learning in medical images: The case of bone age estimation[J]. Healthc Inform Res, 2018, 24(1): 86-92. doi: 10.4258/hir.2018.24.1.86
[11]	LITJENS G, KOOI T, BEJNORDI B E, et al. A survey on deep learning in medical image analysis[J]. Med Image Anal, 2017, 42: 60-88. doi: 10.1016/j.media.2017.07.005
[12]	GHOSAL S, BLYSTONE D, SINGH A K, et al. An explainable deep machine vision framework for plant stress phenotyping[J]. Proc Natl Acad Sci USA, 2018, 115(18): 4613-4618. doi: 10.1073/pnas.1716999115
[13]	TAPAS A. Transfer learning for image classification and plant phenotyping[J]. Int J Res Appl Sci Eng Technol, 2016, 5(11): 2664-2668.
[14]	SLADOJEVIC S, ARSENOVIC M, CULIBRK A A D, et al. Deep neural networks based recognition of plant diseases by leaf image classification[J]. Comput Intel Neurosc, 2016: 3289801.
[15]	ZHANG K, WU Q, LIU A, et al. Can deep learning identify tomato leaf disease?[J]. Advances in Multimedia, 2018, 2018: 1-10.
[16]	RAMCHARAN A, BARANOWSKI K, MCCLOSKEY P, et al. Deep learning for image-based cassava disease detection[J]. Front Plant Sci, 2017, 8: 1-7.
[17]	CHOI D, LEE W, SCHUELLER J K, et al. A precise fruit inspection system for Huanglongbing and other common citrus defects using GPU and deep learning technologies[C]// ISPA. Proceedings of 13th International Conference on Precision Agriculture, St Louis, Missouri: ISPA, 2016, 1-6.
[18]	MOHANTY S P, HUGHES D P, SALATHE M. Using deep learning for image-based plant disease detection[J]. Front Plant Sci, 2016, 7: 1-7.
[19]	LI W, HARTUNG J S, LEVY L. Quantitative real-time PCR for detection and identification of Candidatus Liberibacter species associated with citrus huanglongbing[J]. J Microbiol Meth, 2006, 66(1): 104-115. doi: 10.1016/j.mimet.2005.10.018
[20]	FAWCETT T. An introduction to ROC analysis[J]. Pattern Recogn Lett, 2006, 27(8): 861-874. doi: 10.1016/j.patrec.2005.10.010
[21]	DENG X, LAN Y, XING X, et al. Citrus huanglongbing detection based on image feature extraction and two-stage back propagation neural network modeling[J]. Int J Agr Biol Eng, 2016, 9(6): 20-26.
[22]	李俭川, 秦国军, 温熙森, 等. 神经网络学习算法的过拟合问题及解决方法[J]. 振动、测试与诊断, 2002, 22(4): 260-264. doi: 10.3969/j.issn.1004-6801.2002.04.003
[23]	陶砾, 杨朔, 杨威. 深度学习的模型搭建及过拟合问题的研究[J]. 计算机时代, 2018(2): 14-21.
[24]	REINKING O A. Diseases of economic plants in southern China[J]. Philipp Agric, 1919, 8: 109-135.
[25]	TU C. Notes on disease of economic plants in South China[J]. Lingnan Science Journal, 1932, 11: 489-504.
[26]	吴定尧. 柑橘黄龙病及综合防治[M]. 广州: 广东科技出版社. 2010: 12-20.

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Recognition of Huanglongbing symptom based on deep convolutional neural network

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