Automatic plant recognition using convolutional neural network on malaysian medicinal herbs: the value of data augmentation

(1) Noor Aini Mohd Roslan Mail (School of Computing Sciences, College of Computing, Informatics and Media, Universiti Teknologi MARA, Malaysia)
(2) * Norizan Mat Diah Mail (School of Computing Sciences, College of Computing, Informatics and Media, Universiti Teknologi MARA, Malaysia)
(3) Zaidah Ibrahim Mail (School of Computing Sciences, College of Computing, Informatics and Media, Universiti Teknologi MARA, Malaysia)
(4) Yuda Munarko Mail (Informatics Department, Universitas Muhammadiyah Malang, Indonesia)
(5) Agus Eko Minarno Mail (Informatics Department, Universitas Muhammadiyah Malang, Indonesia)
*corresponding author

Abstract


Herbs are an important nutritional source for humans since they provide a variety of nutrients. Indigenous people have employed herbs, in particular, as traditional medicines since ancient times. Malaysia has hundreds of plant species; herb detection may be difficult due to the variety of herb species and their shape and color similarities. Furthermore, there is a scarcity of support datasets for detecting these plants. The main objective of this paper is to investigate the performance of convolutional neural network (CNN) on Malaysian medicinal herbs datasets, real data and augmented data. Malaysian medical herbs data were obtained from Taman Herba Pulau Pinang, Malaysia, and ten kinds of native herbs were chosen. Both datasets were evaluated using the CNN model developed throughout the research. Overall, herbs real data obtained an average accuracy of 75%, whereas herbs augmented data achieved an average accuracy of 88%. Based on these findings, herbs augmented data surpassed herbs actual data in terms of accuracy after undergoing the augmentation technique.

Keywords


Convolutional neural network (CNN); Deep learning; Malaysian medicinal herbs; Data augmentation

   

DOI

https://doi.org/10.26555/ijain.v9i1.1076
      

Article metrics

Abstract views : 393 | PDF views : 183

   

Cite

   

Full Text

Download

References


[1] H. Q. Cap, K. Suwa, E. Fujita, S. Kagiwada, H. Uga, and H. Iyatomi, “A deep learning approach for on-site plant leaf detection,” Proc. - 2018 IEEE 14th Int. Colloq. Signal Process. its Appl. CSPA 2018, pp. 118–122, May 2018, doi: 10.1109/CSPA.2018.8368697.

[2] J. W. Tan, S. W. Chang, S. Abdul-Kareem, H. J. Yap, and K. T. Yong, “Deep Learning for Plant Species Classification Using Leaf Vein Morphometric,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 17, no. 1, pp. 82–90, Jan. 2020, doi: 10.1109/TCBB.2018.2848653.

[3] N. A. M. Roslan, N. Mat Diah, Z. Ibrahim, H. M. Hanum, and M. Ismail, “Automatic Plant Recognition: A Survey of Relevant Algorithms,” 2022 IEEE 18th Int. Colloq. Signal Process. Appl. CSPA 2022 - Proceeding, pp. 5–9, 2022, doi: 10.1109/CSPA55076.2022.9782022.

[4] S. Mutalib, N. M. Azlan, M. Yusoff, S. A. Rahman, and A. Mohamed, “Plant selection system,” Proc. - Int. Conf. Comput. Sci. its Appl. ICCSA 2008, pp. 33–38, 2008, doi: 10.1109/ICCSA.2008.33.

[5] M. A. F. Azlah, L. S. Chua, F. R. Rahmad, F. I. Abdullah, and S. R. W. Alwi, “Review on Techniques for Plant Leaf Classification and Recognition,” Comput. 2019, Vol. 8, Page 77, vol. 8, no. 4, p. 77, Oct. 2019, doi: 10.3390/COMPUTERS8040077.

[6] Q. Wang, F. Qi, M. Sun, J. Qu, and J. Xue, “Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques,” Comput. Intell. Neurosci., vol. 2019, 2019, doi: 10.1155/2019/9142753.

[7] J. Liu and X. Wang, “Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model,” Plant Methods, vol. 16, no. 1, pp. 1–16, Jun. 2020, doi: 10.1186/S13007-020-00624-2.

[8] M. Sardogan, A. Tuncer, and Y. Ozen, “Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm,” UBMK 2018 - 3rd Int. Conf. Comput. Sci. Eng., pp. 382–385, Dec. 2018, doi: 10.1109/UBMK.2018.8566635.

[9] N. Sabri, M. Mukim, Z. Ibrahim, N. Hasan, and S. Ibrahim, “Computer motherboard component recognition using texture and shape features,” 2018 9th IEEE Control Syst. Grad. Res. Colloquium, ICSGRC 2018 - Proceeding, pp. 121–125, Mar. 2019, doi: 10.1109/ICSGRC.2018.8657579.

[10] Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nat. 2015 5217553, vol. 521, no. 7553, pp. 436–444, May 2015, doi: 10.1038/nature14539.

[11] J. G. A. Barbedo, “Factors influencing the use of deep learning for plant disease recognition,” Biosyst. Eng., vol. 172, pp. 84–91, Aug. 2018, doi: 10.1016/J.BIOSYSTEMSENG.2018.05.013.

[12] P. Jiang, Y. Chen, B. Liu, D. He, and C. Liang, “Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks,” IEEE Access, vol. 7, pp. 59069–59080, 2019, doi: 10.1109/ACCESS.2019.2914929.

[13] Y. Kim, H. Kim, N. Yadav, S. Li, and K. K. Choi, “Low-Power RTL Code Generation for Advanced CNN Algorithms toward Object Detection in Autonomous Vehicles,” Electron. 2020, Vol. 9, Page 478, vol. 9, no. 3, p. 478, Mar. 2020, doi: 10.3390/ELECTRONICS9030478.

[14] N. Sabri, Z. Ibrahim, and N. N. Rosman, “K-means vs. fuzzy C-means for segmentation of orchid flowers,” 2016 7th IEEE Control Syst. Grad. Res. Colloquium, ICSGRC 2016 - Proceeding, pp. 82–86, Jan. 2017, doi: 10.1109/ICSGRC.2016.7813306.

[15] S. T. Cynthia, K. M. Shahrukh Hossain, M. N. Hasan, M. Asaduzzaman, and A. K. Das, “Automated detection of plant diseases using image processing and faster R-CNN algorithm,” 2019 Int. Conf. Sustain. Technol. Ind. 4.0, STI 2019, Dec. 2019, doi: 10.1109/STI47673.2019.9068092.

[16] K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Comput. Electron. Agric., vol. 145, pp. 311–318, Feb. 2018, doi: 10.1016/J.COMPAG.2018.01.009.

[17] J. A. Perez, F. Deligianni, D. Ravi, and G. Yang, "Artificial Intelligence and Robotics," Robot. Auton. Syst., p. 56, 2017. doi : 10.31256/WP2017.1.

[18] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278–2323, 1998, doi: 10.1109/5.726791.

[19] M. Z. Alom et al., “A State-of-the-Art Survey on Deep Learning Theory and Architectures,” Electron. 2019, Vol. 8, Page 292, vol. 8, no. 3, p. 292, Mar. 2019, doi: 10.3390/ELECTRONICS8030292.

[20] S. S. Hari, M. Sivakumar, P. Renuga, S. Karthikeyan, and S. Suriya, “Detection of Plant Disease by Leaf Image Using Convolutional Neural Network,” Proc. - Int. Conf. Vis. Towar. Emerg. Trends Commun. Networking, ViTECoN 2019, Mar. 2019, doi: 10.1109/VITECON.2019.8899748.

[21] J. Chen, J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkaran, “Using deep transfer learning for image-based plant disease identification,” Comput. Electron. Agric., vol. 173, p. 105393, Jun. 2020, doi: 10.1016/J.COMPAG.2020.105393.

[22] M. Mehdipour Ghazi, B. Yanikoglu, and E. Aptoula, “Plant identification using deep neural networks via optimization of transfer learning parameters,” Neurocomputing, vol. 235, pp. 228–235, Apr. 2017, doi: 10.1016/J.NEUCOM.2017.01.018.

[23] R. Takahashi, T. Matsubara, and K. Uehara, “Data Augmentation Using Random Image Cropping and Patching for Deep CNNs,” IEEE Trans. Circuits Syst. Video Technol., vol. 30, no. 9, pp. 2917–2931, Sep. 2020, doi: 10.1109/TCSVT.2019.2935128.

[24] Z. Meng, X. Guo, Z. Pan, D. Sun, and S. Liu, “Data Segmentation and Augmentation Methods Based on Raw Data Using Deep Neural Networks Approach for Rotating Machinery Fault Diagnosis,” IEEE Access, vol. 7, pp. 79510–79522, 2019, doi: 10.1109/ACCESS.2019.2923417.

[25] N. M. Diah, M. L. M. Jahim, N. A. M. Roslan, Z. Ibrahim, and A. Abdullah, “Development of Mobile Application for Plant Disease Recognition using Convolutional Neural Network Method,” 2021 6th IEEE Int. Conf. Recent Adv. Innov. Eng. ICRAIE 2021, Feb. 2022, doi: 10.1109/ICRAIE52900.2021.9704033.

[26] N. Van Hieu and N. L. H. Hien, “Automatic plant image identification of Vietnamese species using deep learning models,” Int. J. Eng. Trends Technol. - IJETT, vol. 68, no. 4, pp. 25–31, Apr. 2020, doi: 10.14445/22315381/IJETT-V68I4P205S.

[27] L. Mookdarsanit and P. Mookdarsanit, “Thai Herb Identification with Medicinal Properties Using Convolutional Neural Network,” Suan Sunandha Sci. Technol. J., vol. 6, no. 2, pp. 34–40, 2019, Accessed: Apr. 03, 2023. [Online]. Available at : li02.tci-thaijo.org.

[28] Y. Zhao, Z. Sun, E. Tian, C. Hu, H. Zong, and F. Yang, “A CNN Model for Herb Identification Based on Part Priority Attention Mechanism,” Conf. Proc. - IEEE Int. Conf. Syst. Man Cybern., vol. 2020-October, pp. 2565–2571, Oct. 2020, doi: 10.1109/SMC42975.2020.9283189.

[29] Y. Sun, Y. Liu, G. Wang, and H. Zhang, “Deep Learning for Plant Identification in Natural Environment,” Comput. Intell. Neurosci., vol. 2017, 2017, doi: 10.1155/2017/7361042.

[30] B. P. Gyires-Tóth, M. Osváth, D. Papp, and G. Szucs, "Deep learning for plant classification and content-based image retrieval," Cybern. Inf. Technol., vol. 19, no. 1, pp. 88-100, 2019, doi: 10.2478/CAIT-2019-0005.doi : 10.2478/cait-2019-0005.

[31] C. Hu, X. Liu, Z. Pan, and P. Li, “Automatic detection of single ripe tomato on plant combining faster R-CNN and intuitionistic fuzzy set,” IEEE Access, vol. 7, pp. 154683–154696, 2019, doi: 10.1109/ACCESS.2019.2949343.

[32] K. K. Leong and L. L. Tze, “Plant Leaf Diseases Identification using Convolutional Neural Network with Treatment Handling System,” 2020 IEEE Int. Conf. Autom. Control Intell. Syst. I2CACIS 2020 - Proc., pp. 39–44, Jun. 2020, doi: 10.1109/I2CACIS49202.2020.9140103.

[33] A. Fuentes, S. Yoon, S. C. Kim, and D. S. Park, “A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition,” Sensors 2017, Vol. 17, Page 2022, vol. 17, no. 9, p. 2022, Sep. 2017, doi: 10.3390/S17092022.

[34] H. Zhu, Q. Liu, Y. Qi, X. Huang, F. Jiang, and S. Zhang, “Plant identification based on very deep convolutional neural networks,” Multimed. Tools Appl., vol. 77, no. 22, pp. 29779–29797, Nov. 2018, doi: 10.1007/S11042-017-5578-9.

[35] W. S. Jeon and S. Y. Rhee, “Plant Leaf Recognition Using a Convolution Neural Network,” Int. J. Fuzzy Log. Intell. Syst., vol. 17, no. 1, pp. 26–34, Mar. 2017, doi: 10.5391/IJFIS.2017.17.1.26.




Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
   andri.pranolo.id@ieee.org (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0