IDENTIFICATION OF SOME TYPES OF LONGAN (THROUGH LEAVES)  USING IMAGE AND DEEP LEARNING TECHNOLOGY

Lê Thị Điểm; Nguyễn Đức Thiện; Trương Quốc Bảo

doi:10.34238/tnu-jst.6946

IDENTIFICATION OF SOME TYPES OF LONGAN (THROUGH LEAVES) USING IMAGE AND DEEP LEARNING TECHNOLOGY

About this article

Received: 17/11/22 Revised: 11/01/23 Published: 11/01/23

Authors

1. Le Thi Diem, Soc Trang Vocational College
2. Nguyen Duc Thien, Soc Trang Vocational College
3. Truong Quoc Bao , Can Tho University

Abstract

In the Mekong Delta region, besides rice being the most important tree, longan has been planted in a large area with various families, bringing high income to the farmers. Each of them has different properties in harvesting time, flowering, and supervising. In fact, the farmers must regularly monitor, take care and rely on their experience to choose the right time to intervene in each type of longan for increasing productivity. From longan pictures taken in the gardens, it is not easy to determine the type of longan excluding knowledgeable people and scientists. Therefore, it is necessary to help apply technology to assess the kind of longan-by-leaf photographs taken of longan. The study used data set of 3 types of longan leaves, namely Ido, Thach Kiet, and Dimocarpus Longan Lour in the total of 2182 collected pictures. Using deep learning techniques with the VGG16 model to train the obtained data, the accuracy result was 98.3%. Thus, the research results can help agronomists and researchers have special measures to support and cooperate with the farmers in classifying and orienting to plant suitable longan with the highest efficiency.

Keywords

Convolutional Neural Network; VGG16; Deep learning; Computer vision; Transfer learning

Full Text:

PDF (Tiếng Việt)

References

[1] M. Vilasini and P. Ramamoorthy, “CNN approaches for classification of Indian leaf species using smartphones,” Comput. Mater. Contin., vol. 62, no. 3, pp. 1445–1472, 2020, doi: 10.32604/cmc.2020.08857.

[2] J. Ahmad, K. Muhammad, I. Ahmad, W. Ahmad, M. L. Smith, L. N. Smith, D. K. Jain, H. Wang, and I. Mehmood, “Visual features based boosted classification of weeds for real-time selective herbicide sprayer systems,” Comput. Ind., vol. 98, pp. 23–33, 2018, doi: 10.1016/j.compind.2018.02.005.

[3] H. A. Atabay, “Article a Convolutional Neural Network With a New,” Iioab J., vol. 7, no. October 2016, pp. 226–231, 2017.

[4] N. Srisook, O. Tuntoolavest, P. Danphitsanuparn, V. Pattana-anake, and F. J. J. Joseph, “Convolutional Neural Network Based Nutrient Deficiency Classification in Leaves of Elaeis guineensis Jacq,” Int. J. Comput. Inf. Syst. Ind. Manag. Appl., vol. 14, pp. 19–27, 2022.

[5] 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.

[6] P. Hridayami, I. K. G. D. Putra, and K. S. Wibawa, “Fish species recognition using VGG16 deep convolutional neural network,” J. Comput. Sci. Eng., vol. 13, no. 3, pp. 124–130, 2019, doi: 10.5626/JCSE.2019.13.3.124.

[7] D. Albashish, R. Al-Sayyed, A. Abdullah, M. H. Ryalat, and N. Ahmad Almansour, “Deep CNN Model based on VGG16 for Breast Cancer Classification,” in 2021 International Conference on Information Technology, ICIT 2021 - Proceedings, 2021, pp. 805–810. doi: 10.1109/ICIT52682.2021.9491631.

[8] J. Pardede, B. Sitohang, S. Akbar, and M. L. Khodra, “Implementation of Transfer Learning Using VGG16 on Fruit Ripeness Detection,” Int. J. Intell. Syst. Appl., vol. 13, no. 2, pp. 52–61, 2021, doi: 10.5815/ijisa.2021.02.04.

[9] E. Bisong, “Google Colaboratory,” in Building Machine Learning and Deep Learning Models on Google Cloud Platform, E. Bisong, Ed. Berkeley, CA: Apress, 2019, pp. 59–64. doi: 10.1007/978-1-4842-4470-8_7.

[10] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and F.-F. Li, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015, doi: 10.1007/s11263-015-0816-y.

[11] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–14, 2015.

[12] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, Jun. 2017, doi: 10.1145/3065386.

[13] S. Li, Y. Zhao, R. Varma, O. Salpekar, P. Noordhuis, T. Li, A. Paszke, J. Smith, B. Vaughan, P. Damania, and S. Chintala, “PyTorch Distributed: Experiences on Accelerating Data Parallel Training,” Proc. VLDB Endow., vol. 13, no. 12, pp. 3005–3018, 2020, doi: 10.14778/3415478.3415530.

DOI: https://doi.org/10.34238/tnu-jst.6946

Refbacks

There are currently no refbacks.



Remember me