Review
Year 2018, Volume: 2 Issue: 2, 40 - 50, 29.12.2018
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, Volume: 2 Issue: 2, 40 - 50, 29.12.2018Abstract
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.
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.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Review Articles |
| Authors |
|
| Publication Date | December 29, 2018 |
| Published in Issue | Year 2018Volume: 2 Issue: 2 |
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