Crowdsourcing for the Identification and Conservation of the Floral Diversity of Nepal: A Technological Perspective

  • Gajendra Sharma Department of Computer Science and Engineering Kathmandu University, Dhulikhel, Nepal (NP)
  • Subarna Adhikari Department of Computer Science and Engineering Kathmandu University, Dhulikhel, Nepal (NP)
Keywords: Plant identification, machine learning, crowdsourcing, floral diversity, citizen science

Viewed = 145 time(s)


Nepal is rich in biodiversity in terms of both flora and fauna. While significant effort has been given to the conservation of wild animals, rare or otherwise, same cannot be said for the floral diversity of the country. In fact, due to significant challenges, the floral diversity of the country remains largely unexplored. The system proposed in this paper tries to overcome those challenges by using technology to aid the collection of information about the floral diversity of the country by crowdsourcing at a local level, using the image data collected for the plant identification by using machine learning or through expert users/volunteers.


Download data is not yet available.


N. Nhan, D. Binh, N. Hoang, V. Hai, T. Hai L. Lan, L. Score-based Fusion Schemes for Plant Identification from Multi-organ Images. VNU Journal of Science: Computer Science and Communication Engineering, 34 (2018).

P.K. Paudel, B.P. Bhattarai, P. Kindlmann. An Overview of the Biodiversity in Nepal. In: Kindlmann P. (eds) Himalayan Biodiversity in the Changing World. Springer, Dordrecht, 2012.

Background information about Nepal. (n.d.). Retrieved May 19, 2019, from

H. Gao, G. Barbier, R. Goolsby. Harnessing the Crowdsourcing Power of Social Media for Disaster Relief. IEEE Intelligent Systems, 26(2011), 10-14.

Ushahidi Staff. Crisis Mapping Haiti: Some Final Reflections [Blog Post]. Retrieved March 12, 2019, from, (2010, April 14).

M. Zook, M. Graham, T. Shelton, S.P. Gorman. Volunteered Geographic Information and Crowdsourcing Disaster Relief : A Case Study of the Haitian Earthquake, 2012.

Capati, M.K. (2015, January 31). 8 Crowdsourcing Mobile Apps That Are Shaping Our Digital Habits. Retrieved May 19, 2019, from

T. Hsu, C. Lee, L. Chen. An interactive flower image recognition system. Multimedia Tools and Applications, 53 (2010), 53-73.

D . Zilli, O. Parson, G.V. Merrett, A. Rogers. A Hidden Markov Model-Based Acoustic Cicada Detector for Crowdsourced Smartphone Biodiversity Monitoring. J. Artif. Intell. Res., 51 (2013), 805-827.

T.T Nguyen,. T. Le, H. Vu, H., V. Hoang, T. Tran. Crowdsourcing for botanical data collection towards to automatic plant identification: A review. Computers and Electronics in Agriculture, 155(2018), 412-425.

S. Bertrand, G. Cerutti, L. Tougne. Bark Recognition to Improve Leaf-based Classification in Didactic Tree Species Identification. VISIGRAPP, 2017.

A. He, X. Tian. Multi-organ plant identification with multi-column deep convolutional neural networks. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 002020-002025, 2016.

H. Goëau, P. Bonnet, J. Barbe, V. Bakic, A. Joly, J. Molino, D. Barthélémy, N. Boujemaa, N. Multi-organ plant identification. MAED@ACM Multimedia, 2012.

Ye, Y., Chen, C.L., Li, C., Fu, H., & Chi, Z. A computerized plant species recognition system. Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2004., 723-726.

A. Ehsanirad, A. Plant Classification Based on Leaf Recognition, 2010.

S.G. Wu, F.S. Bao, E.Y. Xu, Y. Wang, Y. Chang, Q. Xiang, Q. A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network. 2007 IEEE International Symposium on Signal Processing and Information Technology, 2007, 11-16.

How to Cite
G. Sharma and S. Adhikari, “Crowdsourcing for the Identification and Conservation of the Floral Diversity of Nepal: A Technological Perspective”, J. Appl. Sci. Eng. Technol. Educ., vol. 1, no. 2, pp. 119-123, Jun. 2020.