Modern Detection and Description Methods for Natural Plants Recognition
Green planet is one of the Earth’s names which is known as a terrestrial planet and also can be named the fifth largest planet of the solar system as another scientific interpretation. Plants do not have a constant and steady distribution all around the world, and even plant species’ variations are not the same in one specific region. Presence of plants is not only limited to one field like botany; they exist in different fields such as literature and mythology and they hold useful and inestimable historical records. No one can imagine the world without oxygen which is produced mostly by plants. Their influences become more manifest since no other live species can exist on earth without plants as they form the basic food staples too. Regulation of water cycle and oxygen production are the other roles of plants. The roles affect environment and climate. Plants are the main components of agricultural activities. Many countries benefit from these activities. Therefore, plants have impacts on political and economic situations and future of countries. Due to importance of plants and their roles, study of plants is essential in various fields. Consideration of their different applications leads to focus on details of them too. Automatic recognition of plants is a novel field to contribute other researches and future of studies. Moreover, plants can survive their life in different places and regions by means of adaptations. Therefore, adaptations are their special factors to help them in hard life situations. Weather condition is one of the parameters which affect plants life and their existence in one area. Recognition of plants in different weather conditions is a new window of research in the field. Only natural images are usable to consider weather conditions as new factors. Thus, it will be a generalized and useful system. In order to have a general system, distance from the camera to plants is considered as another factor. The other considered factor is change of light intensity in environment as it changes during the day. Adding these factors leads to a huge challenge to invent an accurate and secure system. Development of an efficient plant recognition system is essential and effective. One important component of plant is leaf which can be used to implement automatic systems for plant recognition without any human interface and interaction. Due to the nature of used images, characteristic investigation of plants is done. Leaves of plants are the first characteristics to select as trusty parts. Four different plant species are specified for the goal to classify them with an accurate system. The current paper is devoted to principal directions of the proposed methods and implemented system, image dataset, and results. The procedure of algorithm and classification is explained in details. First steps, feature detection and description of visual information, are outperformed by using Scale invariant feature transform (SIFT), HARRIS-SIFT, and FAST-SIFT methods. The accuracy of the implemented methods is computed. In addition to comparison, robustness and efficiency of results in different conditions are investigated and explained.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1340270Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 435
 N. Sakai, S. Yonekawa, and A. Matsuzaki, “Two dimensional image analysis of the shape of rice and its applications to separating varieties”, Journal of Food Engineering, Vol. 27, 1996, pp. 397-407.
 A. J. Perez, F. Lopez, J. V. Benlloch, and S. Christensen, “Color and shape analysis techniques for weed detection in cereal fields”, Computers and Electronics in Agriculture, Vol. 25, 2000, pp. 197-212.
 A. Del Bimho, P. Pala, and S. Santini, “Image retrieval by elastic matching of shapes and image patterns,” in Proc. 1996 Int. Conf. Multimedia Computing and Systems, Hiroshima, Japan, June 1996, pp. 215–218.
 R. Mehrotra and J. E. Gary, “Feature-based retrieval of similar shapes,” in Proc. 9th Int. Conf. Data Engineering, Vienna, Austria, Apr. 1993, pp. 108–115.
 W. Niblack and J. Yin, “Pseudo-distance measure for 2d shapes based on turning angle,” in Proc. IEEE Int. Conf. Image Processing, Vol. 3, Washington, DC, USA, Oct. 1995, pp. 352–355.
 E. Saber and A. M. Tekalp, “Image query-by-example using region based shape matching,” Proc. SPIE, vol. 2666, 1996, pp. 200–211.
 S. Sclaroff, “Deformable prototypes for encoding shape categories in image databases,” Pattern Recognition., Vol. 30, no. 4, Apr. 1997, pp. 627–641.
 S. Sclaroff and A. P. Pentland, “Modal matching for corresponding and recognition,” IEEE Trans. Pattern Anal. Machine Intell., Vol. 17, June 1995, pp. 545–561.
 S. Abbasi, F. Mokhtarian, and J. Kittler, “Reliable classification of Chrysanthemum leaves through curvature scale space”, Lecture Notes in Computer Science, Vol. 1252, 1997, pp. 284-295.
 C-L Lee, and S-Y Chen, “Classification of leaf images”, 16th IPPR Conference on Computer Vision, Graphics and Image Processing (CVGIP), 2003, pp. 355-362.
 Lowe, D. G., “Object Recognition from Local Scale-Invariant Features”, in Proc. Of the International Conference on Computer Vision, 1999, pp. 1150-1157.
 F. Estrada, A. Jepson, and D. Fleet, “Local Features Tutorial”, http://www.cs.toronto.edu/~jepson/csc2503/tutSIFT04.pdf, Accessed: Dec. 01, 2016.
 Herbert Bay, Tinne Tuytelaars and Luc Van Gool, “SURF: Speeded Up Robust Features”. Computer Vision – ECCV 2006, Lecture Notes in Computer Science, 2006. 3951: pp. 404-417.
 Masoud Fathi Kazerouni, Jens Schlemper, and Klaus-Dieter Kuhner, “Comparison of Modern Description Methods for the Recognition of 32 Plant Species”, Signal & Image Processing: An International Journal (SIPIJ), Vol. 6, No. 2, April 2015, pp.1-13.
 Hossam M. Zawbaa, Mona Abbass, Sameh H. Basha, Maryam Hazman, Abul Ella Hassenian, “An Automatic Flower Classification Approach Using Machine Learning Algorithms”, Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on. IEEE, 2014, 2014, pp. 895-901.
 E. Rosten, T. Drummond. “Machine Learning for High-Speed Corner Detection,” in European Conference on Computer Vision, Vol. 1, May 2006, pp. 430–443.
 Masoud Fathi Kazerouni, Jens Schlemper and Klaus-Dieter Kuhnert; “Efficient Modern Description Methods by Using SURF Algorithm for Recognition of Plant Species”. Advances in Image and Video Processing, Vol. 3 No 2, April (2015); pp: 10-24.
 C. Harris, M. Stephens, “A Combined Corner and Edge Detector”, in Proceedings of the Fourth Alvey Vision Conference, 1988. pp. 147-151
 L. Fei-Fei, R. Fergus, and A. Torralba. “Recognizing and Learning Object Categories, CVPR 2007 short course”, 2007.
 S. Wu, F. Bao, E. Xu, Y.-X. Wang, Y.-F. Chang, and Q.-L. Xiang, “A leaf recognition algorithm for plant classification using probabilistic neural network,” In 2007 IEEE international symposium on signal processing and information technology, IEEE, 2007, pp. 11 -16.
 G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray, “Visual categorization with bags of keypoints,” In Workshop on statistical learning in computer vision, ECCV, Vol. 1, no. 1-22, 2004, pp. 1-2.
 D. Larlus and F. Jurie. “Latent mixture vocabularies for object categorization”, In Proceedings of the British Machine Vision Conference, Vol. 3, 2006, pp. 959-968.
 J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid, “Local features and kernels for classification of texture and object categories: A comprehensive study”, 2006, pp. 13–13.
 D. Gokalp and S. Aksoy, “Scene classification using bag-of-regions representations”. In proceedings of CVPR, 2007, pp. 1–8.
 S. Smith, J. Brady, “SUSAN—A new approach to low level image processing,” International Journal of Computer Vision, Vol. 23, No.1, 1997, pp. 45-48.
 Suryadeep Roy, S. S Tripathy, “A Novel Approach for the Detection of Malaria Parasites and Measure its Severity Using Image Processing and Fuzzy Logic,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 5, Issue 4, April 2016, pp. 3080-3088.
 D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, Vol. 60, No. 2, Nov. 2004, pp. 91-110.
 T. Leung and J. Malik, “Representing and recognizing the visual appearance of materials using three-dimensional textons”. International Journal of Computer Vision 43 (1), 2001, pp. 29–44.
 V. Vapnik, The natural of statistical theory. New York: Springer-Verlag, 1995.
 S. Abe, “Support vector machines for pattern classification,” Vol. 53., London: Springer-Verlag, 2005.
 C. Burges, “A tutorial on support vector machines for pattern recognition,” Data mining knowledge discovery 2 Vol. 2, 1998, pp 121-167.
 V. Kumar, M. Steinbach, and P. N. Tan, "Introduction to data mining," Pearson, Addison Wesley, London, 2006.
 J. Hyuk Hong, and C. Sung Bae, “A probabilistic multi-class strategy of one-vs.-rest support vector machines for cancer classification,” Neuro computing Vol. 71, 2008, pp. 3275-3281.
 Powers, David M W (2011). “Evaluation: From Precision, Recall and F-Measure to ROC, informedness, Markedness & Correlation,” (PDF). Journal of Machine Learning Technologies 2 (1), pp. 37–63.
 “Precision-recall — scikit-learn 0.18.1 documentation,” 2010. (Online). Available: http://scikit-learn.org/stable/auto_examples/model_selection/ plot_precision_recall.html. Accessed: Dec. 13, 2016.