Archives of Computational Methods in Engineering

, Volume 26, Issue 4, pp 933–960 | Cite as

A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification

  • K. K. ThyagharajanEmail author
  • I. Kiruba Raji
Original Paper


Plants are fundamentally important to life. Key research areas in plant science include plant species identification, weed classification using hyper spectral images, monitoring plant health and tracing leaf growth, and the semantic interpretation of leaf information. Botanists easily identify plant species by discriminating between the shape of the leaf, tip, base, leaf margin and leaf vein, as well as the texture of the leaf and the arrangement of leaflets of compound leaves. Because of the increasing demand for experts and calls for biodiversity, there is a need for intelligent systems that recognize and characterize leaves so as to scrutinize a particular species, the diseases that affect them, the pattern of leaf growth, and so on. We review several image processing methods in the feature extraction of leaves, given that feature extraction is a crucial technique in computer vision. As computers cannot comprehend images, they are required to be converted into features by individually analyzing image shapes, colors, textures and moments. Images that look the same may deviate in terms of geometric and photometric variations. In our study, we also discuss certain machine learning classifiers for an analysis of different species of leaves.


Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Datta R, Joshi D, Li JA, Wang JZ (2008) Image retrieval: ideas, influences and trends of new age. ACM Comput Surv. CrossRefGoogle Scholar
  2. 2.
    Du J-X, Wang X-F, Zhang G-J (2006) Leaf shape based plant species. Recogn Appl Math Comput 185:883–893. zbMATHCrossRefGoogle Scholar
  3. 3.
    Macleod N, Benfield M, Culverhouse P (2010) Time to automate identification. Nature 467:154–155. CrossRefGoogle Scholar
  4. 4.
    Babatunde O, Armstrong L, Diepeveen D, Leng J (2015) A survey of computer-based vision systems for automatic identification of plant species. J Agric Inform 6(1):61–71. CrossRefGoogle Scholar
  5. 5.
    Cope JS, Corney D, Clark JY, Remagnino P, Wilkin P (2012) Plant species identification using digital morphometrics: a review. Expert Syst Appl 39:7562–7573. CrossRefGoogle Scholar
  6. 6.
    Waldchen J, Mader P (2016) Plant species identification using computer vision techniques: a systematic literature review. Arch Comput Methods Eng. zbMATHCrossRefGoogle Scholar
  7. 7.
    Pauwels EJ, de Zeeuw PM, Ranguelova EB (2009) Computer-assisted tree taxonomy by automated image recognition. Eng Appl Artif Intell 22(1):26–31. CrossRefGoogle Scholar
  8. 8.
    Pham N-H, Le T-L, Grard P, Nguyen V-N (2013) Computer aided plant identification system. In: 2013 International conference on computing, management and telecommunications (ComMan-Tel), pp 134–139Google Scholar
  9. 9.
    Rejeb Sfar A, Boujemaa N, Geman D (2013) Identification of plants from multiple images and botanical idkeys. In: Proceedings of the 3rd ACM conference on international conference on multimedia retrieval, ACM, New York, NY, USA (ICMR’13), pp 191–198.
  10. 10.
    Ellis B, Ash A, Hickey LJ, Johnson K, Wilf P, Wing S (2009) Manual of leaf architecture. Smithsonian Institution. ISBN: 0-9677554-0-9Google Scholar
  11. 11.
    Sharma S, Gupta C (2015) A review of plant recognition methods and algorithms. Int J Innov Res Adv Eng (IJIRAE) 2(6):2349-2163Google Scholar
  12. 12.
    Minu RI, Thyagharajan KK (2011) Automatic image classification using SVM classifier. CIIT Int J Data Min Knowl Eng 3:559–563.Google Scholar
  13. 13.
    Thyagharajan KK, Minu RI (2013) Prevalent color extraction and indexing. Int J Eng Technol 5(6):4841–4849Google Scholar
  14. 14.
    Thyagharajan KK, Minu RI (2012) Multimodal ontology search for semantic image retrieval. ICTACT J Image Video Process 3:473–478CrossRefGoogle Scholar
  15. 15.
    Caglayan A, Guclu O, Can A (2013) A plant recognition approach using shape and color features in leaf images. In: Petrosino A (ed) Image analysis and processing ICIAP 2013, vol 8157. Lecture Notes in Computer Science. Springer, Berlin, pp 161–170. CrossRefGoogle Scholar
  16. 16.
    Park J, Hwang E, Nam Y (2008) Utilizing venation features for efficient leaf Image Retrieval. J Syst Softw 81:71–82. CrossRefGoogle Scholar
  17. 17.
    Nam Y, Yung E, Kim D (2008) A similarity based leaf image retrieval scheme and venation feature. J Comput Vis Image Underst 110:245–259. CrossRefGoogle Scholar
  18. 18.
    Grinblat GL, Uzal LC, Larese MG, Granitto PM (2016) Deep learning for plant identification using vein morphological patterns. Comput Electron Agric 127:418–424. CrossRefGoogle Scholar
  19. 19.
    Bauer J, NikoSunderhauf PP (2007) Comparing several implementations of two recently published feature detectors. Proc Int Conf Intell Autom Syst 40:143–148. CrossRefGoogle Scholar
  20. 20.
    Lavania S, Matey PS (2014) Leaf recognition using contour based edge detection and sift algorithm. In: 2014 IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–4.
  21. 21.
    Chen Y, Lin P, He Y (2011) Velocity representation method for description of contour based shape classification of weed leaf images. Biosyst Eng 109:186–195. CrossRefGoogle Scholar
  22. 22.
    Laga H, Kurtek S, Srivastava A, Golzarian M, Miklavcic SJ (2012) A Riemannian elastic metric for shape-based plant leaf classification. In: 2012 International conference on digital image computing techniques and applications (DICTA), pp 1–7.
  23. 23.
    Mounie S, Yahiaoui I, Verroust Blondet A (2013) A shape based approach for leaf classification using multiscale triangular representation. In: ACM international conference on multimedia retrieval, pp 127–134.
  24. 24.
    Wang B, Brown D, Gao Y, La Salle J (2015) MARCH: a multi scale arch height descriptor for mobile retrieval leaf images. Inf Sci 302:132–148. CrossRefGoogle Scholar
  25. 25.
    De Souza MMS, Medeiros FNS, Ramalho GLB, de Paula IC, Oliveria INS (2016) Evolutionary optimization of multiscale descriptor for shape analysis. Expert Syst Appl 63(c):375–385. CrossRefGoogle Scholar
  26. 26.
    Chaki J, Parekh R, Bhattacharya S (2015) Plant leaf recognition using texture and shape features with neural classifiers. Pattern Recogn Lett 58:61–68. CrossRefGoogle Scholar
  27. 27.
    Cao J, Wang B, Brown D (2016) Similarity based leaf image retrieval using multiscale R-angle description. Inf Sci 374:51–64. CrossRefGoogle Scholar
  28. 28.
    Sangle S, Shirsat K, Bhosle V (2013) Shape based plant leaf classification system using android. Int J Eng Res Technol 2(8):1900–1907Google Scholar
  29. 29.
    Rahmani ME, Amine A, RedaHamou M (2015) Plant leaves classification. In: The first international conference on big data, small data, linked data, open data, pp 75–80. ISBN:978-1-61208-445-9Google Scholar
  30. 30.
    Knight D, Painter J, Potter M (2010) Automatic plant leaf classification for a mobile field guideGoogle Scholar
  31. 31.
    Thangirala S, Rani J (2015) Perception based on its incline and pier using centroid delineation pitch of leaf. Int J Res Comput Commun Technol 4(3):154–157Google Scholar
  32. 32.
    Bong MF, Sulong GB, Rahim MSM (2013) recognition of leaf based on its tip and base using centroid contour gradient. IJCSI Int J Comput Sci Issues 10(2):477–482Google Scholar
  33. 33.
    Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24(4):509–522. CrossRefGoogle Scholar
  34. 34.
    Mounie S, Yahiaoui I, Verroust Blondet A (2012) Advanced shape context for plant species Identification using leaf image retrieval. In: ACM international conference on multimedia retrieval. Hongkong, China ACMGoogle Scholar
  35. 35.
    Ling H, Jacobs DW (2007) Shape classification using the inner distance. IEEE Trans Pattern Anal Mach Intell 29(2):286–299. CrossRefGoogle Scholar
  36. 36.
    Bellhumer PN, Chen D, Feiner S, Jacobs DW, John Kress W, Ling H, Loppez I, Ramamoorthi R (2008) Searching the World’s Herbaria: a system for visual identification of plant species. Lecture Notes on Computer Science, pp 116–129Google Scholar
  37. 37.
    Zhang SW, Zhao MR, Wang XF (2012b) Plant classification based on multilinear independent component analysis. In: Proceedings of the 7th international conference on advanced intelligent computing theories and applications: with aspects of artificial intelligence (ICIC’11), Springer, Berlin, pp 484–490. Google Scholar
  38. 38.
    Kumar N, Bellhumer PN, Biswas A, Jacobs DW, Kress WJ, Lopez I, Soares JVB (2012) Leafsnap: a computer vision system for plant species identification. Lecture notes in Computer Science, pp 502–516Google Scholar
  39. 39.
    Reul C, Toepfer M, Puppe F (2016) Cross dataset evaluation of feature extraction of feature extraction techniques for leaf classification. Int J Artif Intell Appl 7:1–19. CrossRefGoogle Scholar
  40. 40.
    Carranza Rojas J, Mata Montero E (2016) Combining leaf shape and texture of costa rica plant species identification. CLEI Electr J 19(7):1–29. CrossRefGoogle Scholar
  41. 41.
    Swain KC, Norremark M, Ramus N et al (2011) Weed identification using an automated active shape matching (AASM) technique. Biosyst Eng 110(4):450–457. CrossRefGoogle Scholar
  42. 42.
    Cerutti G, Tongue L, Coquin D, Vacavant A (2013) Curvature scale based contour understanding for leaf margin shape recognition and species identification. In: International conference on computer vision theory and applications, vol 1, pp 277–284Google Scholar
  43. 43.
    Cerutti G, Tongue L, Coquin D, Vacavant A (2014) Leaf margin as sequences: a structural approach to leaf identification. Pattern Recogn Lett 49:177–184. CrossRefGoogle Scholar
  44. 44.
    Du J-X, Huang D-S, Wang X-F, Gu X (2006) Computer-aided plant speciesidentification (CAPSI) based on leafshape matching technique. Trans Inst Meas Control 28(3):275–284CrossRefGoogle Scholar
  45. 45.
    Gwo C-H, Wei YL (2013) Rotary matching of edge features for leaf recognition. Comput Electr Agricu 91:124–134. CrossRefGoogle Scholar
  46. 46.
    Prakash N, Sarkar A (2015) Development of shape based leaf categorization. ISOR J Comput Eng 17(1):48–53Google Scholar
  47. 47.
    Corney David PA, Lillian Tang H, Clark JY, Yin H, Jin J (2012) Automating digital leaf measurement: the tooth, the whole tooth, and nothing but the tooth. PLoS ONE 7(8):e42112. CrossRefGoogle Scholar
  48. 48.
    Jin T, Hou X, Li P, Zhou F (2015) A novel method of automatic plant species identification using sparse representation of leaf tooth features. PLoS ONE 10(10):e0139482. CrossRefGoogle Scholar
  49. 49.
    Asrani K, Jain R (2013) Contour based retrieval for plant species. Int J Image Graph Signal Process Hong Kong 5(9):29–35. CrossRefGoogle Scholar
  50. 50.
    Cho SI, Lee DS, Jeong JY (2002) Weed plant discrimination by machine vision and artificial network. Bio Syst Eng 83(3):275–280. CrossRefGoogle Scholar
  51. 51.
    Singh K, Gupta I, Gupta S (2010) SVM BDT PNN and Fourier moment technique for classification of leaf shape. Int J Signal Process Image Process Pattern Recogn 3(4):67–78Google Scholar
  52. 52.
    Wu Q, Zhou C, Wang C (2006) Feature extraction and automatic recognition of plant leaf using artificial neural network. In: Proceedings of advanced computer technology, pp 47–50Google Scholar
  53. 53.
    Dornbusch T, Andrieu B (2010) Lamina2shape—an image processing tool for an eplicit description of lamina shape tested on winter wheat(Triticum aestivum L.). Comput Electron Agric 70:217–224. CrossRefGoogle Scholar
  54. 54.
    Golzarian MR, Frick RA (2011) Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis. Plant Methods 7:28CrossRefGoogle Scholar
  55. 55.
    Hossain J, Amin MA (2010) Leaf shape identification based plant biometrics. In: 2010 13th International conference on computer and information technology (ICCIT), pp 458–463.
  56. 56.
    Wu SG, Bao FS, Xu EY, Wang Y-X, Cheng Y-F, Xiang Q-L (2007) A leaf recognition algorithm for plant classification using probabilistic neural network. In: IEEE international symposium on signal processing and information technology, pp 1–6.
  57. 57.
    Tzionas P, Papadakis SE, Manolakis D (2005) Plant leaves classification based on morphological features and a fuzzy surface selection technique. In: Fifth international conference on technology and automation, Thessaloniki, Greece, pp 365–370Google Scholar
  58. 58.
    Kadir A, Nugroho LE, Susanto A, Santosa PI (2011) Leaf classification using shape, color and texture features. Int J Comput Trends Technol July–August:225–230Google Scholar
  59. 59.
    Lee KB, Hong KS (2013) An implementation of leaf recognition system using leaf vein and shape. Int J Bio-Sci Bio-Technol 5(2):57–66. CrossRefGoogle Scholar
  60. 60.
    Singh S, Bhamrah MS (2015) Leaf identification using feature extraction and neural network. Int J Electr Commun Eng 10(5):134–140. CrossRefGoogle Scholar
  61. 61.
    Altartouri H, Abu DA, Maizer A, HashemTamimi RA (2015) Computerized extraction of morphological and geometrical features for plants with compound leaves. J Theor Appl Inf Technol 81(3):474–480Google Scholar
  62. 62.
    Sharma S, Gupta C (2015) Recognition of plant species based on leaf images using multilayer feed forward neural network. Int J Innov Res Adv Eng 6(2):104–110Google Scholar
  63. 63.
    Mzoughi O, Yahiaoui I, Boujemaa N, Zagrouba E (2013b) Automated semantic leaf image categorization by geometric analysis. In: 2013 IEEE international conference on multimedia and expo (ICME), pp 1–6.
  64. 64.
    Kalyoncu C, Toygar O (2015) Geometric leaf classification. Comput Vis Image Underst 133:102–109. CrossRefGoogle Scholar
  65. 65.
    Akif A, Khan MF (2015) Automatic classification of plants based on their leaves. Biosyst Eng 139:66–75. CrossRefGoogle Scholar
  66. 66.
    Aptoula E, Yanikoglu B (2013) Morphological features for leaf based plant recognition. In: 2013 20th IEEE international conference on image processing (ICIP), pp 1496–1499.
  67. 67.
    Chaki J, Parekh R, Bhattacharya S (2015b) Recognition of whole and deformed plant leaves using statistical shape features and neuro-fuzzy classifier. In: 2015 IEEE 2nd international conference on recent trends in information systems (ReTIS), pp 189–194.
  68. 68.
    Arribas JI, Sanchez Ferrero GV, Ruiz G, Gomez-gil J (2011) Leaf classification in sunflower crops by computer vision and neural networks. Comput Electr Agric 78:9–18. CrossRefGoogle Scholar
  69. 69.
    Pandey D, Singh P (2014) Image CLEF: analysis of plant identification task based on shape parameter. Int J Emerg Res Manag Technol 3(5):22–212Google Scholar
  70. 70.
    Pushpa BR, Anand C, MithuinNambiar P (2016) Ayurvedic plant species recognition using statistical parameters on leaf images. Int J Appl Eng Res 11(7):5142–5147Google Scholar
  71. 71.
    Hati S, Sajeevan G (2013) Plant recognition from leaf image through artificial neural network. Int J Comput Appl 62(17):15–18. CrossRefGoogle Scholar
  72. 72.
    Dutta L, Basu TK (2013) Extraction and optimization of leaves images of mango trees and classification using ANN. Int J Recent Adv Eng Technol 1(3):46–51Google Scholar
  73. 73.
    Wang Z, Sun X, Zhang Y, Yihg Z, Ma Y (2016) Leaf recognition based on PCNN. Neural Comput Appl 27:899–908. CrossRefGoogle Scholar
  74. 74.
    Liu Q, Wang Y, Ma Y (2009) Image feature extraction and recognition based on adaptive unit linking pulse coupled neural networks. In: IEEE 10th international conference on computer aided industrial design and conceptual design, pp 2065–2068.
  75. 75.
    Wang Z, Sun X, Ma Y, Zhang H, Ma Y, Xie W (2014) Plant recognition based on intersecting Cortial model. In: International joint conference on neural network.
  76. 76.
    Caballero C, Aranda MC (2010) Plant species identification using leaf image retrieval. In: Proceedings of the ACM international conference on image and video retrieval (CIVR’10). ACM, New York, NY, USA, pp 327–334.
  77. 77.
    Xia C, Lee J-M, Li Y, Song Y-H, Chung B-K, Chon T-S (2013) Plant leaf detection using modified active shape. Biosyst Eng 116:23–35. CrossRefGoogle Scholar
  78. 78.
    Backes AR, Casanova D, Bruno OM (2008) A complex network based approach for boundary shape analysis. Pattern Recogn 42(1):54–67. zbMATHCrossRefGoogle Scholar
  79. 79.
    Beghin T, Cope JS, Remangnino P, Barman S (2010) Shape and texture based plant leaf classification, advanced concepts for intelligent vision systems. Lect Notes Comput Sci 6475:345–353. CrossRefGoogle Scholar
  80. 80.
    Chen Y, Lin P, He Y, Zhenghao X (2011) Classification of broadleaf weed images using gabor wavelets and Lie group structure of region covariance on Riemanian manifolds. Biosyst Eng 109:220–227. CrossRefGoogle Scholar
  81. 81.
    Bruno OM, de Oliveira Plotze R, Falvo M, de Castro M (2008) Fractal dimension applied to plant identification. Inf Sci 178(12):2722–2733. MathSciNetCrossRefGoogle Scholar
  82. 82.
    de Oliveira R, Plotze MF, Pádua JG, Bernacci LC, Vieira MLC, Oliveira GCX, Bruno OM (2005) Leaf shape analysis using the multiscale minkowski fractal dimension, a new morphometric method : a study with Passiflora. Can J Bot 83:287–301. CrossRefGoogle Scholar
  83. 83.
    Jobin A, Nair MS, Tatavarti R (2012) Plant identification based on fractal refinement technique (FRT). Procedia Technol 6:171–179. CrossRefGoogle Scholar
  84. 84.
    Muchtar M, Suciati N, Fatichah C (2016) Fractal dimension and lacunarity combination for plant leaf classification. J Comput Sci Inf 9(2):96–105. CrossRefGoogle Scholar
  85. 85.
    Casanova D, de Mesquita Sá JJ, Junior OB (2009) Plant leaf identification using gabor wavelets. Int J Imaging Syst Technol 19(3):236–243. CrossRefGoogle Scholar
  86. 86.
    Vijayalakshmi B, Mohan V (2016) Kernel based PSO and FRVM: an automatic plant leaf type detection using texture, shape and color features. Comput Electron Agric 125:9–112. CrossRefGoogle Scholar
  87. 87.
    Florindo JB, da Silva NR, Romualdo LM et al (2014) Brachiaria species identification using imaging Techniques based on fractal descriptors. Comput Electron Agric 103:48–54. CrossRefGoogle Scholar
  88. 88.
    Les T, Kruk M, Osowski M (2013) Objects classification using fractal dimension and shape based on leaves classification. Warsaw University of Technology and Life sciences, WarsawGoogle Scholar
  89. 89.
    Husin Z, Shakaff AYM, Aziz AHA, Farook RSM, Jaafar MN, Hashim U, Harun A (2012) Embedded portable device for herb leaves using image processing and neural network algorithms. Comput Electr Agric 89:18–29. CrossRefGoogle Scholar
  90. 90.
    Arun CH, Sam Emmanuel WR, Durairaj C (2013) Texture feature extraction for identification of medicinal plants and comparison of different classifiers. Int J Comput Appl 62(12):1–8. CrossRefGoogle Scholar
  91. 91.
    Venkatesh SK, Raghavendra R (2011) Local gabor phase quantization scheme for robust leaf classification. In: 2011 Third national conference on computer vision, pattern recognition, image processing and graphics (NCVPRIPG), pp 211–214.
  92. 92.
    Qi X, Xiao R, Li C-G, Qiao Y, Guo J, Tang X (2014) Pairwise rotation invariant co-occurrence local binary pattern. IEEE Tran Pattern Anal Mach Intell 36:2199–2212. CrossRefGoogle Scholar
  93. 93.
    Sule M, Matas J (2014) Texture based leaf identification. Research Report of CMP, Crez Technical University. (10):CTU-CMP-2014-10Google Scholar
  94. 94.
    Naresh YG, Nagendraswamy HS (2016) Classification of medicinal plants: an approach using modified LBP with symbolic representation. Neurocomputing 173:1789–1797. CrossRefGoogle Scholar
  95. 95.
    Tang Z, YuanCheng S, MengJooEr FQ, Zhang L, Zhou J (2015) A local binary pattern based texture descriptors for classification of tea leaves. NeuroComputing 168:1011–1023. CrossRefGoogle Scholar
  96. 96.
    Qiuyan L,Wenfa Q (2015) Multiscale local binary pattern based on path integral for texture classification. In: IEEE international conference on image processing, pp 26–30.
  97. 97.
    Cote M, Albu AB (2015) Robust texture classification by aggregating pixel-based LBP statistics. IEEE Signal Process Lett 22(11):2102–2106. CrossRefGoogle Scholar
  98. 98.
    Sumathi CS, Senthil Kumar AV (2012) Edge and texture fusion for plant leaf classification. Int J Comput Sci Telecommun 3(6):6–9Google Scholar
  99. 99.
    Sana OM, Jaya R (2015) Ayurvedic herb detection using image processing. Int J Comput Sci Inf Technol Res 3(4):134–139Google Scholar
  100. 100.
    Siricharoen P, Scotney B, Morrow P, Parr G (2016) A lightweight mobile system for crop disease diagnosis. In: International conference image analysis and recognition, pp 783–791Google Scholar
  101. 101.
    Wang S, Wu Q, He X, Yang J, Wang Y (2015) Local N array pattern and its extension for texture classification. IEEE Trans Circuits Syst Video Technol 25(9):1495–1506. CrossRefGoogle Scholar
  102. 102.
    XuanWang J, Guo F (2014) Feature extraction algorithm based on dual-scale decomposition and local binary descriptors for plant leaf recognition. Dig Signal Process 34:101–107. CrossRefGoogle Scholar
  103. 103.
    Zhang J, Zhao H, Liang J (2013) Continuous rotation invariant local descriptors for texton dictionary-based texture classification. Comput Vis Image Underst 117(1):56–75CrossRefGoogle Scholar
  104. 104.
    Minu RI, Thyagharajan KK (2014) Semantic rule based image visual feature ontology creation. Int J Autom Comput 11(5):489–499. CrossRefGoogle Scholar
  105. 105.
    Minu RI, Thyagharajan KK (2012) A novel approach to build image ontology using texton. Advances in Intelligent Systems and Computing, vol 182, pp 333–339. Springer, Berlin. ISBN: 978-3-642-32062-0 (Print) 978-3-642-32063-7 (Online), ISSN: 2194-5357Google Scholar
  106. 106.
    Guo Z, Li Q, Zhang L, You J, Zhang D, Liu W (2013) Is local dominant orientation necessary for the classification of rotation invariant texture? Neuro Computing 116:182–191. CrossRefGoogle Scholar
  107. 107.
    Abdolvahab Eshani Rad (2010) Plant classification based on leaf recognition. Int J Comput Sci Inf Secur 8(4):78–81Google Scholar
  108. 108.
    Paramanand C, Rajagopalan AN (2014) Shape from sharp motion-blurred image pair. Int J Comput Vis 107:272–292. CrossRefGoogle Scholar
  109. 109.
    Trczinksi T, Christoudias M, Fua P, Lepetit V (2013) Boosting binary key point descriptors. IEEE Conf Comput Vis Pattern Recogn. CrossRefGoogle Scholar
  110. 110.
    Le TL, Tran D-T, Hoang V-N (2014) Fully automatic leaf based plant identification, application of Vietnamese medicinal plant search. In: Proceedings of the fifth symposium on information and communication technology, pp 146–154.
  111. 111.
    Le TL, Tran D-T, Hoang V-N (2014) Kernel descriptor based plant leaf identification. Image Process Theory Tools Appl. CrossRefGoogle Scholar
  112. 112.
    Zhao ZQ, Ma L-H, Chen Y, Wu X, Tang Y, Chen CLP (2015) ApLeaf: an efficient android based leaf identification system. Neurocomputing 151:1112–1119. CrossRefGoogle Scholar
  113. 113.
    Horaisova K, Kukal J (2016) Leaf classification from binary image via artificial intelligence. Biosyst Eng 42:83–100. CrossRefGoogle Scholar
  114. 114.
    Arai K, Abdullah IN, Okumura H (2013) Identification of ornamental plant functioned as medicinal plant based on redundant discrete wavelet transformation (IJARAI). Int J Adv Res Artif Intell 2(3):61–64. CrossRefGoogle Scholar
  115. 115.
    Abdul Kadir LE, Susanto NA, Santosa PI (2011) A comparative experiment of several shape methods in recognizing plants. Int J Comput Sci Inf Technol 3(5):256–263. CrossRefGoogle Scholar
  116. 116.
    Kadir A (2015) Leaf identification using fourier descriptors and other shape features. Gate Comput Vis Pattern Recogn 1(1):3–7. MathSciNetCrossRefGoogle Scholar
  117. 117.
    Arivazhagan S, Gowri L, Ganesan K (2010) Rotation and scale invariant texture classification using log polar and Ridgelet transform. J Pattern Recogn Res 5(1):131–139. CrossRefGoogle Scholar
  118. 118.
    Derrode S, Ghorbel F (2001) Robust and efficient Fourier-Mellin transform approximations for gray-level image reconstruction and complete invariant description. Comput Vis Image Underst 83(1):57–78. zbMATHCrossRefGoogle Scholar
  119. 119.
    Neto JC, Meyer GE, Jones DD, Samal AK (2006) Plant species identification using elliptic Fourier leaf shape analysis. Comput Electron Agric 50:121–134. CrossRefGoogle Scholar
  120. 120.
    Du J-X, Zhai C-M, Wang Q-P (2013) Recognition of plant leaf Image based on fractal dimension features. Neuro Comput 116:150–156. CrossRefGoogle Scholar
  121. 121.
    Pallavi P, Veena D (2014) Leaf recognition based on feature extraction and Zernike moments. Int J Innov Res Comput Commun Eng, 67–73. ISSN:2320-09801Google Scholar
  122. 122.
    Charters J, Wang Z, Chi Z, Tsoi AC, Feng DD (2014) Eagle: a novel descriptor for identifying plant species using leaf lamina vascular features. In: 2014 IEEE international conference on multimedia and expo workshops (ICMEW), pp 1–6.
  123. 123.
    Zulkifli Z, Saad P, Mohtar IA (2011) Plant leaf identification using moment invariants & general regression neural network. In: 2011 11th International conference on hybrid intelligent systems (HIS), pp 430–435.
  124. 124.
    Adsule BR, Bhattad JM (2015) Leaves classification using SVM using neural network disease identification. Int J Innov Res Comput Commun Eng 3(6):5488–5495CrossRefGoogle Scholar
  125. 125.
    Sainin MS, Alfred R (2009) Half leaf shape feature extraction for leaf identification. In: First Malaysian international conference on artificial intelligenceGoogle Scholar
  126. 126.
    Bagalkote IS, Vibhute AS, More BM (2014) Texture analysis using DWT for grape plant species classification. J Bot Sci 3(3):34–40Google Scholar
  127. 127.
    Anami BS, Pujari JD, Yakkundimath R (2011) Identification and classification of normal and affected agriculture/horticulture produce based on combined color and texture feature extraction. Int J Comput Appl Eng Sci 1(3):356–360Google Scholar
  128. 128.
    Sathish V, Ramesh K (2015) Identification and classification of plant leaf disease. Int J Adv Res Sci Eng 4(1):978–983Google Scholar
  129. 129.
    Ravisankar AM, Mohanapriya M (2016) Classification of name based on leaf recognition using BT and ED algorithm. Int J Comput Appl Technol Res 5(4):191–197Google Scholar
  130. 130.
    Nandyal S, Bagewadi S (2013) Automated identification of plant species from images of leaves and flowers used in the diagnosis of arthritis. Int J Res Eng Adv Technol 1(5):1–10Google Scholar
  131. 131.
    Zhai C-M, Du J-X (2008) Applying extreme learning machine to plant species identification. In: International conference on information and automation, 2008. ICIA 2008, pp 879–884.
  132. 132.
    Sharma S, Gupta C (2015) Recognition of plant species based on leaf images using multilayer Feed Forward neural network. Int J Innov Res Adv Eng 6(2):104–110Google Scholar
  133. 133.
    Arunpriya C, Thanamani AS (2015) Fuzzy inference system algorithm of plant classification for tea leaf recognition. Indian J Sci Technol 8(S7):179–184CrossRefGoogle Scholar
  134. 134.
    Nikesh P, Nidheesh P, Shashidhar MS (2013) Leaf identification using geometric and biometric features. ASM’s Int J Ongoing Res Manag IT, 1–7. ISSN:2320-0065Google Scholar
  135. 135.
    Rahmani ME, Amine A, RedaHamou M (2015) Plant leaves classification. In: The first international conference on big data, small data, linked data, open data, 75–80. ISBN:978-1-61208-445-9Google Scholar
  136. 136.
    Elhariri E, El-Bendary N, Hassanien AE (2014) Plant classification system based on leaf features. In: 2014 9th International conference on computer engineering systems (ICCES), pp 271–276.
  137. 137.
    Wang X-F, Huang D-S, Ji-Xiang D, Huan X, Heutte L (2008) Classification of plant leaf images with complicated background. Appl Math Comput 205:916–926MathSciNetzbMATHGoogle Scholar
  138. 138.
    Nesaratnam J, BalaMurugan C (2015) Identifying leaf in a natural image using morphological characters. In: 2015 International conference on innovations in information, embedded and communication systems (ICIIECS), pp 1–5.
  139. 139.
    Prasad S, Kudiri KM, Tripathi RC (2011) Relative subimage based features for leaf recognition using support vector machine. In: Proceedings of the 2011 international conference on communication, computing & security, ACM, New York, NY, USA (ICCCS’11), pp 343–346.
  140. 140.
    Tsolaidis D, Kosmopoulos DI, Papadourakis G (2014) Plant leaf recognition using zernike moments and histogram of oriented gradients. Lecture Notes on Computer Science, pp 406–417. CrossRefGoogle Scholar
  141. 141.
    Mebastin HK, Paliwal J, Jayas DS (2012) Evaluation of variations in the shape of grain types using principal components and analysis of the elliptic Fourier descriptors. Comput Electr Agric 80:63–70. CrossRefGoogle Scholar
  142. 142.
    Sainin MS, Ahmad F, Alfred R (2016) Improving the identification and classification of Malaysian medicinal leaf images using ensemble method. In: International conference on ICT for transformation, pp 1–6Google Scholar
  143. 143.
    Sainin MS, Alfred R, Ghazali TK (2014) Malaysian medicinal plant leaf shape identification and classification. In: Knowledge management international conference, pp 578–583Google Scholar
  144. 144.
    Dyrmann M, Karstof H, Midity HS (2016) Plant species classification using deep convolutional network. Biosyst Eng 151:72–80. CrossRefGoogle Scholar
  145. 145.
    Sladojevic S, Arsenovic M, Andala A, Glibrt D, Stefanvoic D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosci. CrossRefGoogle Scholar
  146. 146.
    Rongxiang H, Jia W, Ling H, Huang D (2012) Multiscale distance matrix for fast plant leaf recognition. Image Process IEEE Trans 21(11):4667–4672. MathSciNetzbMATHCrossRefGoogle Scholar
  147. 147.
    Zang S, Lai Y, Dong T, Zhang X-P (2013) Label propagation based supervised locating projection analysis for plant classification. Pattern Recogn 46:1891–1897. CrossRefGoogle Scholar
  148. 148.
    Zang S, KeLei Y (2011) Modified locally linear discriminant embedding for plant leaf recognition. Neurocomputing 74:2284–2290. CrossRefGoogle Scholar
  149. 149.
    Narayan V, Subbarayan G (2014) An optimal feature subset selection using GA for leaf classification. Int Arab J Inf Technol 11(5):447–451Google Scholar
  150. 150.
    Valliammal N, Geethalakshmi SN (2012) An optimal feature subset selection for leaf analysis. World Acad Sci Eng Technol 62(2012):440–445Google Scholar
  151. 151.
    Fong H, Li H (2014) Plant leaves recognition and classification model based on image features and neural network. Int J Comput Sci 11(2):100–104Google Scholar
  152. 152.
    Gu X, Du J-X, Wang X-F (2005) Leaf recognition based on the combination of wavelet transform and Gaussian interpolation. In: Huang DS, Zhang XP, Huang GB (eds) Advances in intelligent computing, vol 3644. Lecture Notes in Computer Science. Springer, Berlin, pp 253–262. CrossRefGoogle Scholar
  153. 153.
    Ahmed N, Khan UG, Asif S (2016) An automatic leaf based plant identification system. Sci Int (Lahore) 28(1):427–430. CrossRefGoogle Scholar
  154. 154.
    Cope JS, Remagnino P (2012) Classifying plant leaves from their margins using dynamic time warping. In: Blanc-Talon J, Philips W, Popescu D, Scheunders P, Zemc KP (eds) Advanced concepts for intelligent vision systems, vol 7517. Lecture Notes in Computer Science. Springer, Berlin, pp 258–267. CrossRefGoogle Scholar
  155. 155.
    Hsiao J-K, Kang L-W, Cha C-L, Lin C-Y (2014) Comparative study of leaf image recognition with a novel learning-based approach. In: 2014 Science and information conference (SAI), pp 389–393.
  156. 156.
    Nguyen QK, Le TL, Pham NH (2013) Leaf based plant identification system for android using surf features in combination with bag of words model and supervised learning. In: 2013 International conference on advanced technologies for communications (ATC), pp 404–407.
  157. 157.
    Sanchez J, Peronnin F, Mensink T, Verbeek J (2013) Image classification with fisher vector: theory and practice. Int J Comput Vis 105:22–245. MathSciNetCrossRefGoogle Scholar
  158. 158.
    Söderkvist OJO (2001) Computer vision classifcation of leaves from Swedish trees. Master’s Thesis, Linkoping UniversityGoogle Scholar
  159. 159.
    Swedish leaf dataset. Last Accessed 26 June 2017
  160. 160.
    Ren X-M, Wan X-F, Zhao Y (2012) An efficient multi-scale overlapped block LBP approach for leaf image recognition. In: Proceedings of the 8th international conference on intelligent computing theories and applications (ICIC’12). Springer, Berlin, pp 237–243. Google Scholar
  161. 161.
    Flavia Dataset. Last Accessed 26 June 2017
  162. 162.
    Harish BS, Hedge A, Venkatesh OP, Spoorthy DG, Sushma D (2013) Classification of plant leaves using morphological features and Zernike moments. In: International conference in computing, communications and informatics.
  163. 163.
  164. 164.
    Lei Y-K, Zou J-W, Dung T, You Z-H, Yuan Y, Hu Y (2014) Orthogonal locally discriminant spline embedding for plant leaf recognition. Comput Vis Image Underst 119:116–126. CrossRefGoogle Scholar
  165. 165.
    UCI Machine Repository. Last Accessed 26 June 2017
  166. 166.
    Silva Pedro FB, Marcal Andre RS, Rubim M, da Silva A (2013) Evaluation of features for leaf discrimination. Lect Notes Comput Sci 7950:197–204CrossRefGoogle Scholar
  167. 167.
    Austrian Federal Forest Dataset. Last Accessed 26 June 2017
  168. 168.
    Smithsonian Leaf dataset. Last Accessed 26 June 2017
  169. 169.
    Leaf snap Database. Last Accessed 26 June 2017
  170. 170.
    Middle European Wood. Last Accessed 26 June 2017
  171. 171.
    Novotny P, Suk T (2013) Leaf recognition of woody species in central Europe. Biosyst Eng 115(4):444–452. CrossRefGoogle Scholar
  172. 172.
    Pl@ntNet. Last Accessed 26 June 2017
  173. 173.
    Yahiaoui I, Mzoughi O, Boujemaa N (2012) Leaf shape descriptor for tree species identification. In: International conference on multimedia and expo, pp 254–259.
  174. 174.
    Liu H, Coquin D, Valet L, Cerutti G (2014) Leaf species classification based on a botanical shape sub-classifier strategy. In: 2014 22nd International conference on pattern recognition(ICPR), pp 1496–1501.
  175. 175.
    Joly A, Goëaua H, Bonnet P, Bakić V, Barbe J, Selmi S, Yahiaoui I, Carré J, Mouysset E, Molino J-F, Boujemaa N, Barthélémy D (2014) Interactive plant identification based on social image data. Ecol Inf 23:22–34. CrossRefGoogle Scholar
  176. 176.
    Backes AR, Bruno OM (2009) Plant leaf identification using multiscale fractal dimension. In: Foggia P, Sansone C, Vento M (eds) Image analysis and processing ICIAP 2009: Lecture Notes in Computer Science, vol 5716. Springer, Berlin, pp 143–150. CrossRefGoogle Scholar
  177. 177.
    Burks TF, Shearer SA, Heath JR, Donohue KD (2005) Evaluation of neural network classifiers for weed species Discrimination. Biosyst Eng 91(3):293–304. CrossRefGoogle Scholar
  178. 178.
    Gwo C-Y, Wei C-H (2013) plant identification through images: using feature extraction of key points on leaf contours. Appl Plant Sci 1(11):1–9. CrossRefGoogle Scholar
  179. 179.
    Cope JS, Remagnino P, Barman S, Wilkin P (2010) Plant texture classification using gabor co-occurrences. In: Bebis G, Boyle R, Parvin B, Koracin D, Chung R, Hammound R, Hussain M, Kar-Han T, Crawfis R, Thalmann D, Kao D, Avila L (eds) Advances in visual computing, vol 6454. Lecture Notes in Computer Science. Springer, Berlin, pp 669–677. CrossRefGoogle Scholar
  180. 180.
    Fotopoulou F, Laskaris N, Economou G, Fotopoulo S (2013) Advanced leaf image etrieval via multidimensional embedding sequence similarity (mess) method. Pattern Anal Appl 16(3):381–392. MathSciNetCrossRefGoogle Scholar
  181. 181.
    Ghasab MAJ, Khamis S, Mohammad F, Fariman HJ (2015) Feature decision-making ant colony optimization system for an automated recognition of plant species. Expert Syst Appl 42(5):2361–2370. CrossRefGoogle Scholar
  182. 182.
    Goeau H, Bonnet P, Joly A, Bakic V, Barthelemy D, Boujemaa N, Molino J-F (2013) The image CLEF 2013 plant identification task. In: Proceedings of the 2nd ACM international workshop on multimedia analysis for ecological data (MAED’13). ACM, New York, pp 23–28.
  183. 183.
    Goëau H, Bonnet P, Joly A, Bakic V, Barthelemy D, Boujemaa N, Molino J-F (2014) Life clef plant identification task 2014. In: Working notes for CLEF 2014 conference, Sheffield, UK, September 15–18, 2014, CEUR-WS, pp 598–615Google Scholar
  184. 184.
    Cerutti G, Togue L, Mille J, Vacavant A, Coquin D (2013) A model based approach for compound leaves understanding and identification. In: International conference on image processing, pp 1471–1475.
  185. 185.
    Yahiaoui I, Mouine S, Verroust A (2013) Plant species recognition using spatial correlation between leaf margin and salient points. In: International conference on image processing.
  186. 186.
    Du J-X, Shao M-W, Zhai C-M, Wang J, Tang Y, Chen CLP (2016) Recognition of leaf image set based on manifold-manifold distance. Neurocomputing 188:131–138. CrossRefGoogle Scholar
  187. 187.
    AbJabal MF, Hamid S, Ahuib S, Ahmad I (2013) Leaf features extraction and recognition approaches to classify plant. J Comput Sci 9(10):1295–1304. CrossRefGoogle Scholar
  188. 188.
    Mohanty P, Pradhan AK, Behera S, Pasaya AK (2015) A real time fast non-soft computing approach towards leaf identification. In: 2014 Proceedings of the 3rd international conference on frontiers of intelligent computing: theory and applications (FICTA). Advances in Intelligent Systems and Computing, vol 327. Springer, Berlin, pp 815–822. Google Scholar
  189. 189.
    Liu N, Kan J-m (2016) Improved deep belief networks and multi feature fusion for leaf identification. Neurocomputing 216:460–467. CrossRefGoogle Scholar
  190. 190.
    Nideesh P, Rajeev A, Nikesh P (2015) Classification of leaf using geometric features. Int J Eng Res Gen Sci 3(2):1185–1190Google Scholar
  191. 191.
    Salve P, Sardesai M, Manza R, Yannawar P (2016) Identification of the plants Based on leaf Shape descriptors. In: Proceedings of the international conference on computer and communication technologies, advances in intelligent systems and computing, vol 379. Google Scholar
  192. 192.
    Rashad M, Desouky B, Khawasik MS (2011) Plants images classification based on textural features using combined classifier. Int J Comput Sci Inf Technol (IJCSIT) 3(4):93–100. CrossRefGoogle Scholar
  193. 193.
    Tekkesinoglu S, Rahim MSM, Rehman A, Amin IM, Saba T (2014) Hevea leaves boundary identification based on morphological transformation and edge detection features. Res J Appl Sci Eng Technol 7(12):2447–2451CrossRefGoogle Scholar
  194. 194.
    Nandyal SS, Govardhan A (2013) Base and apex angles and margin types-based identification and classification from medicinal plants leaves images. Int J Comput Vis Robot 3(3):197–224. CrossRefGoogle Scholar
  195. 195.
    Watcharabutsarakham S, Sinthupinyo W, Kiratiratanapruk K (2012) Leaf classification using structure features and support vector machines. In: 2012 6th International conference on new trends in information science and service science and data mining (ISSDM), pp 697–700Google Scholar
  196. 196.
    Wu H, Wang L, Zhang F, Wen Z (2015) Automatic leaf recognition from a big hierarchical image database. Int J Intell Syst 30(8):871–886. CrossRefGoogle Scholar
  197. 197.
    Xiao X-Y, Hu R, Zhan S-W, Wang X-F (2010) Hog-based approach for leaf classification. In: Proceedings of the advanced intelligent computing theories and applications, and 6th international conference on intelligent computing (ICIC’10). Springer, Berlin, pp 149–155. CrossRefGoogle Scholar
  198. 198.
    Yang L-W, Wang X-F (2012) Leaf image recognition using fourier transform based on ordered sequence. In: Huang DS, Jiang C, Bevilacqua V, Figueroa J (eds) Intelligent computing technology, vol 7389. Lecture notes in Computer Science. Springer, Berlin, pp 393–400. CrossRefGoogle Scholar
  199. 199.
    Yanikoglu B, Aptoula E, Tirkaz C (2014) Automatic plant identification from photographs. Mach Vis Appl 25(6):1369–1383. CrossRefGoogle Scholar
  200. 200.
    Manik FY, Herdiyeni Y, Herliyana EN (2016) Leaf morphlogical feature extraction of digital image Anthocephalus cadamba. TELKOMNIKA 14(2):630–637. CrossRefGoogle Scholar

Copyright information

© CIMNE, Barcelona, Spain 2018

Authors and Affiliations

  1. 1.RMD Engineering CollegeKavaraipettai, ChennaiIndia

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