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Year 2018, , 40 - 50, 29.12.2018

Noor Ahmed Shaikh Ghulam Ali Mallah İsmail Rakıp Karaş , Abdullah Emin Akay

https://doi.org/10.38088/jise.498910

Abstract

References

  • [1] Chupezi, T.J. , Ndoye, O., Tchatat M. and Chikamai. B. (2009). Processing and Marketing of Non-wood Forest Products: Potential Impacts and Challenges in Africa. Discov. Innov., 21:60-65.
  • [2] Dembner, S.A. and Perlis, A. (1999). Non-wood Forest Products and Income Generation. Vol. 50.
  • [3] Durst, P.B., Ulrich, W. and Kashio, M. (1994). Regional office for Asia and the Pacific (RAPA) food and agriculture organization of the United Nations. Vol. 28. pp. 151-161.
  • [4] Iqbal, M. (1991). Non-timber forest products: their income-generation potential for rural women in North West Frontier Province (Pakistan). International Labour Organization and Government of NWFP. Peshawar.
  • [5] RAPA, (1987). Forest based rural enterprises in Pakistan. Regional Office for Asia and the Pacific (RAPA), Food and Agricultural Organization of the United Nations. Bangkok. 88 p.
  • [6] Salada, L.L., Hickey, K. and Salada, T. (1995). Comparing manual plant identification systems, Hort Technology, 5(2):159.
  • [7] Bylaiah, V. (2014). Leaf recognition and matching with Matlab. MSc Thesis, san Diego State University, 42 p.
  • [8] Neeraj, K. (2012). Leafsnap: A computer vision system for automatic plant species identification. ECCV 2012, pp. 502–516.
  • [9] Wu, G. (2007). A leaf recognition algorithm for plant classification using probabilistic neural network, in signal processing and information technology. IEEE International Symposium on Signal Processing and Information Technology, 15-18 December, Giza, Egypt.
  • [10] Prastiva D.S. and Herdiyeni, Y. (2013). International Journal on Advanced Science, Engineering and Information Technology. 3(2):5-9.
  • [11] Ridwan, A. (2007). Pengukuran usability aplikasi menggunakan evaluasi heuristic. Jurnal Informatika Komputer, 12(3):220-222.
  • [12] Le, T., Tran, D. and Hoang, V. (2014). Fully automatic leaf-based plant identification, application for Vietnamese medicinal plant search. SoICT ’14, December 04 – 05, Hanoi, Vietnam.
  • [13] Bengio, Y., Courville, A. and Vincent, P. (2013). Representation learning: a review and new perspectives, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1798–1828.
  • [14] Sun, Y., Liu, Y., Wang, G. and Zhang, H. (2017). Deep Learning for Plant Identification in Natural Environment” Computational Intelligence and Neuroscience Volume 2017, Article ID 7361042, 6 p. https://doi.org/10.1155/2017/7361042.
  • [15] He, K., Zhang, X., Ren, S. and Sun, J. (2016). Deep residual learning for image recognition in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’16), pp.770-778, Las Vegas, Nevada, USA.
  • [16] Dai, J., He, K. and Sun, K. (2016). Instance-aware semantic segmentation via multi-task network cascades, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’16), pp.3150-3158, Las Vegas, Nevada, USA.
  • [17] Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y.X., Chang, Y.F. and Xiang, Q.L. (2007). A leaf recognition algorithm for plant classification using probabilistic neural network. IEEE International Symposium on Signal Processing and Information Technology, 15-18 December, Giza, Egypt.
  • [18] Begue, A., Kowlessur, V., Mahomoodally, F., Singh, U., Pudaruth, S. (2017). Automatic Recognition of Medicinal Plants using Machine Learning Techniques, International Journal of Advanced Computer Science and Applications, 8(4):166-175.

Using Mobile Image Recognition System for Nonwood Species Identification in the Field

Year 2018, , 40 - 50, 29.12.2018

Noor Ahmed Shaikh Ghulam Ali Mallah İsmail Rakıp Karaş , Abdullah Emin Akay

https://doi.org/10.38088/jise.498910

Abstract

Non-wood forest products (NWFPs) provide important source of income for
millions of households world-wide especially for those in rural areas.
Especially nonwood plants are main source of income for rural people in many
parts of the world. These plants are also consumed by local people for health,
nutrition and many other needs. In order to ensure benefits of NWFPs, it is
important to identify nonwood plants and determine their population
distribution. However, plant identification by using manual method is a complex
task to fulfill especially in varying conditions in the field. The leaves are
often used to identify plant species based on their visible characteristics.
Image recognition systems have been developed to identify plant species by
using leaf image. Comparing manual plant identification systems, image
recognition systems are easier and, in most cases, more accurate alternatives.
In recent years, some mobile applications have been developed to assist people
to identify plants by taking pictures of their leaves using smart phones. In
this study, it was aimed to investigate the capabilities of the most common
mobile image recognition systems in identification of plant species based on
leaf images.

Keywords

Image recognition Mobile applications Plant identification Nonwood species

References

  • [1] Chupezi, T.J. , Ndoye, O., Tchatat M. and Chikamai. B. (2009). Processing and Marketing of Non-wood Forest Products: Potential Impacts and Challenges in Africa. Discov. Innov., 21:60-65.
  • [2] Dembner, S.A. and Perlis, A. (1999). Non-wood Forest Products and Income Generation. Vol. 50.
  • [3] Durst, P.B., Ulrich, W. and Kashio, M. (1994). Regional office for Asia and the Pacific (RAPA) food and agriculture organization of the United Nations. Vol. 28. pp. 151-161.
  • [4] Iqbal, M. (1991). Non-timber forest products: their income-generation potential for rural women in North West Frontier Province (Pakistan). International Labour Organization and Government of NWFP. Peshawar.
  • [5] RAPA, (1987). Forest based rural enterprises in Pakistan. Regional Office for Asia and the Pacific (RAPA), Food and Agricultural Organization of the United Nations. Bangkok. 88 p.
  • [6] Salada, L.L., Hickey, K. and Salada, T. (1995). Comparing manual plant identification systems, Hort Technology, 5(2):159.
  • [7] Bylaiah, V. (2014). Leaf recognition and matching with Matlab. MSc Thesis, san Diego State University, 42 p.
  • [8] Neeraj, K. (2012). Leafsnap: A computer vision system for automatic plant species identification. ECCV 2012, pp. 502–516.
  • [9] Wu, G. (2007). A leaf recognition algorithm for plant classification using probabilistic neural network, in signal processing and information technology. IEEE International Symposium on Signal Processing and Information Technology, 15-18 December, Giza, Egypt.
  • [10] Prastiva D.S. and Herdiyeni, Y. (2013). International Journal on Advanced Science, Engineering and Information Technology. 3(2):5-9.
  • [11] Ridwan, A. (2007). Pengukuran usability aplikasi menggunakan evaluasi heuristic. Jurnal Informatika Komputer, 12(3):220-222.
  • [12] Le, T., Tran, D. and Hoang, V. (2014). Fully automatic leaf-based plant identification, application for Vietnamese medicinal plant search. SoICT ’14, December 04 – 05, Hanoi, Vietnam.
  • [13] Bengio, Y., Courville, A. and Vincent, P. (2013). Representation learning: a review and new perspectives, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1798–1828.
  • [14] Sun, Y., Liu, Y., Wang, G. and Zhang, H. (2017). Deep Learning for Plant Identification in Natural Environment” Computational Intelligence and Neuroscience Volume 2017, Article ID 7361042, 6 p. https://doi.org/10.1155/2017/7361042.
  • [15] He, K., Zhang, X., Ren, S. and Sun, J. (2016). Deep residual learning for image recognition in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’16), pp.770-778, Las Vegas, Nevada, USA.
  • [16] Dai, J., He, K. and Sun, K. (2016). Instance-aware semantic segmentation via multi-task network cascades, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’16), pp.3150-3158, Las Vegas, Nevada, USA.
  • [17] Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y.X., Chang, Y.F. and Xiang, Q.L. (2007). A leaf recognition algorithm for plant classification using probabilistic neural network. IEEE International Symposium on Signal Processing and Information Technology, 15-18 December, Giza, Egypt.
  • [18] Begue, A., Kowlessur, V., Mahomoodally, F., Singh, U., Pudaruth, S. (2017). Automatic Recognition of Medicinal Plants using Machine Learning Techniques, International Journal of Advanced Computer Science and Applications, 8(4):166-175.
There are 18 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Review Articles
Authors

Noor Ahmed Shaikh 0000-0001-6328-2760

Ghulam Ali Mallah 0000-0003-3428-9802

İsmail Rakıp Karaş 0000-0001-5934-3161

Abdullah Emin Akay 0000-0001-6558-9029

Publication Date December 29, 2018
Published in Issue Year 2018

Cite

APA Shaikh, N. A., Mallah, G. A., Karaş, İ. R., Akay, A. E. (2018). Using Mobile Image Recognition System for Nonwood Species Identification in the Field. Journal of Innovative Science and Engineering, 2(2), 40-50. https://doi.org/10.38088/jise.498910
AMA Shaikh NA, Mallah GA, Karaş İR, Akay AE. Using Mobile Image Recognition System for Nonwood Species Identification in the Field. JISE. December 2018;2(2):40-50. doi:10.38088/jise.498910
Chicago Shaikh, Noor Ahmed, Ghulam Ali Mallah, İsmail Rakıp Karaş, and Abdullah Emin Akay. “Using Mobile Image Recognition System for Nonwood Species Identification in the Field”. Journal of Innovative Science and Engineering 2, no. 2 (December 2018): 40-50. https://doi.org/10.38088/jise.498910.
EndNote Shaikh NA, Mallah GA, Karaş İR, Akay AE (December 1, 2018) Using Mobile Image Recognition System for Nonwood Species Identification in the Field. Journal of Innovative Science and Engineering 2 2 40–50.
IEEE N. A. Shaikh, G. A. Mallah, İ. R. Karaş, and A. E. Akay, “Using Mobile Image Recognition System for Nonwood Species Identification in the Field”, JISE, vol. 2, no. 2, pp. 40–50, 2018, doi: 10.38088/jise.498910.
ISNAD Shaikh, Noor Ahmed et al. “Using Mobile Image Recognition System for Nonwood Species Identification in the Field”. Journal of Innovative Science and Engineering 2/2 (December 2018), 40-50. https://doi.org/10.38088/jise.498910.
JAMA Shaikh NA, Mallah GA, Karaş İR, Akay AE. Using Mobile Image Recognition System for Nonwood Species Identification in the Field. JISE. 2018;2:40–50.
MLA Shaikh, Noor Ahmed et al. “Using Mobile Image Recognition System for Nonwood Species Identification in the Field”. Journal of Innovative Science and Engineering, vol. 2, no. 2, 2018, pp. 40-50, doi:10.38088/jise.498910.
Vancouver Shaikh NA, Mallah GA, Karaş İR, Akay AE. Using Mobile Image Recognition System for Nonwood Species Identification in the Field. JISE. 2018;2(2):40-5.


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