1. Mata-Montero, E. and Carranza-Rojas, J., “Automated plant species identification: Challenges and opportunities”, IFIP Advances in Information and Communication Technology, Springer, Vol. 481, (2016), 26-36, doi: 10.1007/978-3-319-44447-5_3.
2. Grand-Brochier, M., Vacavant, A., Cerutti, G., Kurtz, C., Weber, J. and Tougne, L., “Tree leaves extraction in natural images: Comparative study of preprocessing tools and segmentation methods”, IEEE Transactions on Image Processing, Vol. 24, No. 5, (2015), 1549-1560, doi: 10.1109/TIP.2015.2400214.
3. Wäldchen, J. and Mäder, P., “Plant species identification using computer vision techniques: A systematic literature review”, Archives of Computational Methods in Engineering, Vol. 25, No. 2, (2018), 507-543, doi: 10.1007/s11831-016-9206-z.
4. Wang, Z., Li, H., Zhu, Y. and Xu, T., “Review of plant identification based on image processing” Archives of Computational Methods in Engineering, Vol. 24, No. 3, (2017), 637-654, doi: 10.1007/s11831-016-9181-4.
5. Zhang, S., Zhang, C. and Wang, X., “Plant species recognition based on global-local maximum margin discriminant projection”, Knowledge-Based Systems, Vol. 200, (2020), doi: 10.1016/j.knosys.2020.105998.
6. Zhang, S., Huang, W., Huang, Y.A. and Zhang, C., “Plant Species Recognition Methods using Leaf Image: Overview”,
Neurocomputing, (2020), doi: 10.1016/j.knosys.2020.105998.
7. Kumar, N., Belhumeur, P.N., Biswas, A., Jacobs, D.W., Kress, W.J., Lopez, I.C. and Soares, J.V., “Leafsnap: A computer vision system for automatic plant species identification”, in European Conference on Computer Vision, Springer, (2012), doi: 10.1007/978-3-642-33709-3_36.
8. Cerutti, G., Tougne, L., Coquin, D. and Vacavant, A., “Curvature-scale-based contour understanding for leaf margin shape recognition and species identification”, International Conference on Computer Vision Theory and Applications (2013), doi: 10.5220/0004225402770284.
9. Nikbakhsh, N., Y. Baleghi, and H. Agahi, “Maximum mutual information and Tsallis entropy for unsupervised segmentation of tree leaves in natural scenes”, Computers and Electronics in Agriculture, Vol. 162, (2019), 440-449, doi: 10.1016/j.compag.2019.04.038.
10. Le, T.-L., D.-T. Tran, and N.-H. Pham. “Kernel descriptor based plant leaf identification”, in 2014 4th International Conference on Image Processing Theory, Tools and Applications (IPTA), IEEE, (2014), doi: 10.1109/IPTA.2014.7001990.
11. Saleem, G., Akhtar, M., Ahmed, N. and Qureshi, W.S., “Automated analysis of visual leaf shape features for plant classification”, Computers and Electronics in Agriculture, Vol. 157, (2019), 270-280, doi: 10.1016/j.compag.2018.12.038.
12. Kaya, A., Keceli, A.S., Catal, C., Yalic, H.Y., Temucin, H. and Tekinerdogan, B., “Analysis of transfer learning for deep neural network based plant classification models”, Computers and Electronics in Agriculture, Vol. 158, (2019), 20-29, doi: 10.1016/j.compag.2019.01.041.
13. Hedjazi, M.A., I. Kourbane, and Y. Genc. “On identifying leaves: A comparison of CNN with classical ML methods”, in 2017 25th Signal Processing and Communications Applications Conference, IEEE, (2017), doi: 10.1109/SIU.2017.7960257.
14. Kamilaris, A. and F.X. Prenafeta-Boldú, “Deep learning in agriculture: A survey”, Computers and Electronics in Agriculture, Vol. 147, (2018), 70-90, doi: 10.1016/j.compag.2018.02.016.
15. Mehri-Dehnavi, H., H. Agahi, and R. Mesiar, “Pseudo-exponential distribution and its statistical applications in econophysics”, Soft Computing, Vol. 23, No. 1, (2019), 357-363, doi: 10.1007/s00500-018-3623-x.
16. Nikbakhsh, N. and Y. Baleghi. “A New Fast Method of Image Segmentation Fusion Using Maximum Mutual Information”, in 2019 27th Iranian Conference on Electrical Engineering, IEEE, (2019), doi: 10.1109/IranianCEE.2019.8786371.
17. Wang, H., Zhang, Y., Nie, R., Yang, Y., Peng, B. and Li, T., “Bayesian image segmentation fusion”, Knowledge-Based Systems, Vol. 71, (2014), 162-168, doi: 10.1016/j.knosys.2014.07.021.
18. Arora, J., K. Khatter, and M. Tushir, “Fuzzy c-means clustering strategies: A review of distance measures”, Software Engineering, Springer, (2019), 153-162, doi: 10.1007/978-981-10-8848-3_15.
19. Comaniciu, D. and P. Meer, “Mean shift: A robust approach toward feature space analysis”, IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol. 5, (2002), 603-619, doi: 10.1109/34.1000236.
20. Chan, T.F., B.Y. Sandberg, and L.A. Vese, “Active contours without edges for vector-valued images”, Journal of Visual Communication and Image Representation, Vol. 11, No. 2, (2000), 130-141, doi:10.1006/jvci.1999.0442.