International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Alejandro Blanco 1, José Sánchez 1, Christian del Carpio 2, Erwin Dianderas 3, Guillermo Kemper 1*

1 Faculty of Engineering, School of Electronic Engineering, Universidad Peruana de Ciencias Aplicadas, Lima 15023, Peru

2 Laboratorio de Investigación en Inteligencia Artificial, Robótica y Procesamiento de Imágenes, Universidad Nacional de Ingeniería, Lima 15333, Peru

3 BOSQUES, Instituto de Investigaciones de la Amazonía Peruana, Iquitos 16001, Peru

 

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ABSTRACT

The identification of timber species is highly important worldwide due to their global trade and the arising requirement of controlling, regulating and monitoring illegal logging. Peru has an important timber trade due to extensive forest resources in the Peruvian Amazon. This context makes it necessary to have technological tools for timber species identification using tree leaves, in an attempt to control deforestation in the Amazon forests. Current identification methods require manual labor, are subjective and inefficient regarding time, cost and precision. Moreover, the scientific literature does not include electronic devices nor algorithms which exclusively identify Peruvian timber species. With this in mind, this work proposes a portable electronic equipment that detects 11 timber species from the Peruvian jungle, based on image processing techniques and convolutional neural networks (CNN). The device consists of a carbon fiber structure, a keyboard, a 7-inch display, internal lighting and a tray for the leaf sample. The algorithm uses color filters, thresholding and other operations to segment the leaf and then input it to a neural network. After comparing the AlexNet, VGG-16 and MobileNet architectures, the last one shows the best performance, with an average precision of 98.64% when identifying the 11 timber species.


Keywords: Portable device, Timber species, Image processing, Convolutional neural networks (CNN), Species identification.


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REFERENCES

  1. Canny, J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 679–698.

  2. Chen, R.C., Saravanarajan, V.S., Hung, H.T. 2021. Monitoring the behaviours of pet cat based on YOLO model and raspberry Pi. International Journal of Applied Science and Engineering, 18, 1–12.

  3. Ferreira, C.A., Inga, J.G., Vidal, O.D., Goytendia, W.E., Moya, S.M., Centeno, T.B., Vélez, A., Gamarra, D., Tomazello-Filho, M. 2021. Identification of tree species from the peruvian tropical amazon “selva central” forests according to wood anatomy. BioResources, 16, 7161–7179.

  4. Ghorui, A., Chatterjee, S., Makkar, R., Pachiyappan, A., Balamurugan, S. 2023. Deployment of CNN on colour fundus images for the automatic detection of glaucoma. International Journal of Applied Science and Engineering, 20, 1–9.

  5. Gonzales, R.C., Woods, R.E. 2018. Digital image processing. fourth edition. In Pearson Education, Inc.

  6. Hassanein, M., Lari, Z. El-Sheimy, N. 2018. A new vegetation segmentation approach for cropped fields based on threshold detection from hue histograms. Sensors, 18, 1–25.

  7. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H. 2017. MobileNets: Efficient convolutional neural networks for mobile vision applications. Retrieved 2023-01-31 from https://www.electronicaplugandplay.com/sistemas-embebidos/product/797-rpi4-4gb

  8. Kang, E., Oh, I.S. 2018. Weak constraint leaf image recognition based on convolutional neural network. International Conference on Electronics, Information and Communication, ICEIC 2018, 2018-Janua, 1–4.

  9. Kingma, D.P., Ba, J.A. 2014. A method for stochastic optimization. Computer Research Repository.

  10. Kirkland, E.J., 2010. Bilinear interpolation. Advanced Computing in Electron Microscopy, 261–263. Springer US.

  11. Krizhevsky, A., Sutskever, I., Hinton, G.E. 2012. ImageNet classification with deep convolutional neural networks. Neural Information Processing Systems, 25.

  12. Maharana, K., Mondal, S., Nemade, B. 2022. A review: Data pre-processing and data augmentation techniques. Global Transitions Proceedings, 3, 91–99.

  13. Monroy-de-Jesús, J., Reyes-Nava, A., Olmos, F. 2019. Clasificador de plantas medicinales por medio de deep learning. Research in Computing Science, 148, 65–78.

  14. Ozturk, S. Akdemir, B. 2017.Automatic leaf segmentation using grey wolf optimizer based neural network. In 2017 Electronics, 1–6.

  15. Raspberry Pi. 2020. Raspberry pi high quality camera. Retrieved 2023-01-31 from https://static.raspberrypi.org/files/product-briefs/Raspberry_Pi_HQ_Camera_ Product_Brief.pdf

  16. Selda, J.D.S., Ellera, R.M.R., Cajayon, L.C., Linsangan, N.B. 2017. Plant identification by image processing of leaf veins. Proceedings of the International Conference on Imaging, Signal Processing and Communication, 40–44.

  17. Singh, G., Aggarwal, N., Gupta, K., Misra, D.K. 2020. Plant identification using leaf specimen. 2020 11th International Conference on Computing, Communication and Networking Technologies, 1–7.

  18. Soetedjo, A. Hendriarianti, E. 2022. A comparative study of vetiveria zizanioides leaf segmentation techniques using visible, infrared, and thermal camera sensors in an outdoor environment. Applied System Innovation, 6, 1.

  19. Song, Y., He, F., Zhang, X. 2019. To identify tree species with highly similar leaves based on a novel attention mechanism for CNN. IEEE Access, 7, 163277–163286.

  20. Srivastava, V., Khunteta, A. 2018. Comparative analysis of leaf classification and recognition by different SVM classifiers. 2018 International Conference on Inventive Research in Computing Applications, 626–631.

  21. Tammina, S. 2019. Transfer learning using VGG-16 with deep convolutional neural network for classifying images. International Journal of Scientific and Research Publications, 9.

  22. Tharwat, A. 2021. Classification assessment methods. Applied Computing and Informatics, 17, 168–192.

  23. Varghese, B.K., Augustine, A., Babu, J.M., Sunny, D., Cherian, S. 2020. INFOPLANT: Plant recognition using convolutional neural networks. 2020 Fourth International Conference on Computing Methodologies and Communication, 800–807.

  24. Vijay, P.P., Patil, N.C. 2016. Gray scale image segmentation using otsu thresholding optimal approach. Journal for Research, 2, 20–24.

  25. Winkel, G., Leipold, S., Buhmann, K., Cashore, B., De Jong, W., Nathan, I., Sotirov, M., Stone, M. 2017. Narrating illegal logging across the globe: Between green protectionism and sustainable resource use. International Forestry Review, 19, 81–97.

  26. Zarrin, I., Islam, S. 2019. Leaf based trees identification using convolutional neural network. 2019 IEEE 5th International Conference for Convergence in Technology, 1–4.


ARTICLE INFORMATION

Received: 2023-07-24
Revised: 2023-09-06
Accepted: 2023-09-18
Available Online: 2023-12-26


Cite this article:

Blanco, A., Sánchez, J., del Carpio, C., Dianderas, E., Kemper, G. 2024. Identification of Peruvian timber species based on image processing and convolutional neural networks. International Journal of Applied Science and Engineering, 21, 2023274. https://doi.org/10.6703/IJASE.202403_21(1).001

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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