Study of digital image processing techniques for leaf disease detection and classification



In this paper, we address a comprehensive study on disease recognition and classification of plant leafs using image processing methods. The traditional manual visual quality inspection cannot be defined systematically as this method is unpredictable and inconsistent. Moreover, it involves a remarkable amount of expertise in the field of plant disease diagnostics (phytopathology) in addition to the disproportionate processing times. Hence, image processing has been applied for the recognition of plant diseases. The paper has been divided into two main categories viz. detection and classification of leafs. A comprehensive discussion on the diseases detection and classification performance is presented based on analysis of previously proposed state of art techniques particularly from 1997 to 2016. Finally, discussed and classify the challenges and some prospects for future improvements in this space.


Computer vision Image analysis Plant leaf diseases Feature extraction Segmentation Classifiers 


  1. 1.
    Abdullah NE, Rahim AA, Hashim H, Kamal K (2007) Classification of rubber tree leaf diseases using multilayer perceptron neural network. In: Fifth student conference on research and development (SCORed), Selangor, 11-12 December. pp. 1–6Google Scholar
  2. 2.
    Abdullakasim W, Powbunthorn K, Unartngam J, Takigawa T (2011) An images analysis technique for recognition of brown leaf spot disease in cassava. Journal of Agricultural Machinery Science 7(2):165–169Google Scholar
  3. 3.
    Aduwo JR, Mwebaze E, Quinn JA (2010) Automated vision-based diagnosis of cassava mosaic disease. In: Industrial Conference on Data Mining-Workshops, pp. 114–122Google Scholar
  4. 4.
    Al-Hiary H, Bani-Ahmad S, Reyalat M, Braik M, ALRahamneh Z (2011) Fast and accurate detection and classification of plant diseases. Int J Comput Appl 17(1):31–38Google Scholar
  5. 5.
    Al-Tarawneh MS (2013) An empirical investigation of olive leaf spot disease using auto-cropping segmentation and fuzzy c-means classification. World Appl Sci J 23(9):1207–1211Google Scholar
  6. 6.
    Anthonys G, Wickramarachchi N (2009) An image recognition system for crop disease identification of paddy fields in Sri Lanka. In: Fourth IEEE international conference on industrial and information systems (ICIIS 2009), Sri Lanka, 28-31 December. pp. 403–407Google Scholar
  7. 7.
    Arivazhagan S, Shebiah RN, Ananthi S, Varthini SV (2013) Detection of unhealthy region of plant leafs and classification of plant leaf diseases using texture features. Agric Eng Int CIGR J 15(1):211–217Google Scholar
  8. 8.
    Asfarian A, Herdiyani Y, Rauf A, Mutaqin KH (2013) Paddy diseases identification with texture analysis using fractal descriptors based on Fourier spectrum. In: International conference on computer, control, informatics and its applications (IC3INA), Jakarta, 19-21 November. pp. 77–81Google Scholar
  9. 9.
    Asraf HM, Nooritawati MT, Rizam MS (2012) A comparative study in kernel-based support vector machine of oil palm leafs nutrient disease. Procedia Engineering 41(2012):1353–1359CrossRefGoogle Scholar
  10. 10.
    Bandi SR, A Varadharajan, Chinnasamy A (2013) Performance evaluation of various statistical classifiers in detecting the diseased citrus leaf. Int J Eng Sci Technol 5(2):298–307Google Scholar
  11. 11.
    Barbedo JGA (2013) Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus 2(1):660CrossRefGoogle Scholar
  12. 12.
    Bashish DA, Braik M, Bani-Ahmad S (2010) A framework for detection and classification of plant leaf and stem diseases. In: IEEE International Conference on Signal and Image Processing, pp.113–118Google Scholar
  13. 13.
    Bentley JW, Boa E, Danielsen S, Franco P, Antezana O, Villarroel B, Rodríguez H, Ferrrufino J, Franco J, Pereira R, Herbas J (2009) Plant health clinics in Bolivia 2000-2009: operations and preliminary results. Food Sec 1(3):371–386CrossRefGoogle Scholar
  14. 14.
    Billah M, Miah MBA, Hanifa A, Amin R (2015) Adaptive neuro fuzzy inference system based tea leaf disease recognition using color wavelet. Commun Appl Electron 3(5):1–4Google Scholar
  15. 15.
    Bin MohamadAzmi MT, Isa NM (2013) Orchid disease detection using image processing and fuzzy logic. In: International conference on electrical, electronics and system engineering (ICEESE), Kuala Lumpur, 4-5 December. pp.37–42Google Scholar
  16. 16.
    Bos L (1970) Symptoms of virus diseases in plants, 2nd edn. VADA, WageningenGoogle Scholar
  17. 17.
    Cai HY, Caswell JL, Prescott JF (2014) Non-culture molecular techniques for diagnosis of bacterial disease in animals: a diagnostic laboratory perspective. Vet Pathol 51(2):341–350CrossRefGoogle Scholar
  18. 18.
    Camargo A, Smith JS (2009) An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosyst Eng 102(1):9–21Google Scholar
  19. 19.
    Chaerle L, Leinonen I, Jones HG, Van Der Straeten D (2007) Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging. J Exp Bot 58(4):773–784CrossRefGoogle Scholar
  20. 20.
    Cunlou L, Gao S, Zhou Z (2013) Maize disease recognition via fuzzy least square support vector machine. J Inf Comput Sci 8(4):316–320Google Scholar
  21. 21.
    Devereux S (2009) Why does famine persist in Africa? Food Sec 1(1):25MathSciNetCrossRefGoogle Scholar
  22. 22.
    Es-saady Y, El Massi I, El Yassa M, Mammass D, Benazoun A (2016) Automatic recognition of plant leaf diseases based on serial combination of two SVM classifiers. In: International Conference on Electrical and Information Technologies, pp.561–566Google Scholar
  23. 23.
    Eun AJC, Huang L, Chew FT, Li SFY, Wong SM (2002) Detection of two orchid viruses using quartz crystal microbalance (QCM) immunosensors. J Virol Methods 99(1):71–79CrossRefGoogle Scholar
  24. 24.
    Fang Y, Umasankar Y, Ramasamy RP (2014) Electrochemical detection of p-ethylguaiacol, a fungi infected fruit volatile using metal oxide nanoparticles. Analyst 139(15):3804–3810CrossRefGoogle Scholar
  25. 25.
    Figueiredo AC, Barroso JG, Pedro LG, Scheffer JJ (2008) Factors affecting secondary metabolite production in plants: volatile components and essential oils. Flavour Fragance J 23(4):213–226CrossRefGoogle Scholar
  26. 26.
    Flood J (2010) The importance of plant health to food security. Food Sec 2(3):215–231CrossRefGoogle Scholar
  27. 27.
    Ghaiwat, SN, Arora N (2014) Detection and classification of plant leaf diseases using image processing techniques: a review. International Journal of Recent Advances in Engineering and Technology 2347–2812Google Scholar
  28. 28.
    González-Fernandez R, Prats E, Jorrín-Novo JV (2010) Proteomics of plant pathogenic fungi. J Biomed Biotechnol 2010:1–36Google Scholar
  29. 29.
    Gui J, Hao L, Zhang Q, Bao X (2015) A new method for soybean leaf disease detection based on modified salient regions. International Journal of Multimedia and Ubiquitous Engineering 10(6):45–52CrossRefGoogle Scholar
  30. 30.
    Guru DS, Mallikarjuna PB, Manjunath S (2011) Segmentation and classification of tobacco seedling diseases. In: Fourth Annual ACM Bangalore Conference, Bangalore, March 25-26Google Scholar
  31. 31.
    Haferkamp MR (1988) Environmental factors affecting plant productivity. Achieving efficient use of rangeland resources. Montana State University Agricultural Experiment Station, Bozeman, pp 27–36Google Scholar
  32. 32.
    He Q, Ma B, Qu D, Zhang Q, Hou X, Zhao J (2013) Cotton pests and diseases detection based on image processing. Indonesian Journal of Electrical Engineering and Computer Science 11(6):3445–3450Google Scholar
  33. 33.
    Hitimana E, Gwun O (2014) Automatic estimation of live coffee leaf infection based on image processing techniques. In: Second international conference on signal, image processing and pattern recognition (SIPP), Sydney, 24 FebuaryGoogle Scholar
  34. 34.
    Huang K-Y (2007) Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features. Comput Electron Agric 57:3–11Google Scholar
  35. 35.
    Husin ZB, Shakaff AY, Aziz AH, Farook RB (2012) Feasibility study on plant chili disease detection using image processing techniques. In: Third IEEE International Conference on Intelligent Systems, Modeling and Simulation (ISMS), Kota Kinabalu, 8-10 Febuary. pp. 291–296Google Scholar
  36. 36.
    Jagtap SB, Shailesh MH (2014) Agricultural plant leaf disease detection and diagnosis using image processing based on morphological feature extraction. IOSR J VLSI Sig Proc 4(5):24–30Google Scholar
  37. 37.
    Jaware TH, Badgujar RD, Patil PG (2012) Crop disease detection using image segmentation. World J Sci Technol 2(4):190–194Google Scholar
  38. 38.
    Jian Z, Wei Z (2010) Support vector machine for recognition of cucumber leaf diseases. In: Second IEEE international conference on advanced computer control, vol 5. IEEE, Shenyang, pp. 264–266Google Scholar
  39. 39.
    Kai S, Zhikun L, Hang S, Chunhong G (2011) A research of maize disease image recognition of corn based on BP networks. In: IEEE Third International Conference on Measuring Technology and Mechatronics Automation, pp.246–249Google Scholar
  40. 40.
    Kavya, RM, Gowda A, Bharathi PT, Virupakshaiah HK (2016) Image processing techniques based plant disease detection. Int J Adv Found Res Computer: Pragmatic Rev 3(8):1–8Google Scholar
  41. 41.
    Keskar PV, Masare SM, Kadam MS, Deoghare SM (2013) Leaf disease detection and diagnosis. Int J Emerg Trends Electr Electron 2(2):28–31Google Scholar
  42. 42.
    Krishnan M, Sumithra MG (2013) A novel algorithm for detecting bacterial leaf scorch (BLS) of shade trees using image processing. In: Eleventh IEEE malaysia international conference on communications (MICC), Kuala Lumpur, 26-28 November. pp. 474–478Google Scholar
  43. 43.
    Kruse OM, Prats-Montalbán JM, Indahl UG, Kvaal K, Ferrer A, Futsaether CM (2014) Pixel classification methods for identifying and quantifying leaf surface injury from digital images. Comput Electron Agric 108:155–165CrossRefGoogle Scholar
  44. 44.
    Kuckenberg J, Tartachnyk I, Noga G (2009) Temporal and spatial changes of chlorophyll fluorescence as a basis for early and precise detection of leaf rust and powdery mildew infections in wheat leafs. Precis Agric 10(1):34–44CrossRefGoogle Scholar
  45. 45.
    Kurniawati NN, Abdullah SNHS, Abdullah S, Abdullah S (2009) Investigation on image processing techniques for diagnosing paddy diseases. In: IEEE International conference of soft computing and pattern recognition (SOCPAR 09), Malacca, 4-7 December. pp. 272–277Google Scholar
  46. 46.
    Kutty SB, Abdullah NE, Hashim H, Kusim AS, Yaakub TN, Yunus PN, Rahman MF (2013) Classification of watermelon leaf diseases using neural network analysis. In: IEEE business engineering and industrial applications colloquium (BEIAC 2013), Langkawi, 7-9 April. pp. 459–464Google Scholar
  47. 47.
    Mahlein AK (2016) Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis 100(2):241–251CrossRefGoogle Scholar
  48. 48.
    Majid K, Herdiyeni Y, Rauf A (2013) I-PEDIA: Mobile application for paddy disease identification using fuzzy entropy and probabilistic neural network. In: IEEE international conference on advanced computer science and information systems (ICACSIS), Bali, 28-29 September. pp. 403–406Google Scholar
  49. 49.
    Martinez A (2007) Georgia plant disease loss estimates. The University of Georgia Cooperative Extension Bulletin. University of Georgia, AthensGoogle Scholar
  50. 50.
    Massi IE, Saddy YE, Yassa ME, Mammass D, Benazon A (2015) Serial combination of two classifiers for automatic recognition of the damages and symptoms on plant leaf. In : IEEE Third World Conference on Complex Systems, pp.1–6Google Scholar
  51. 51.
    Meunkaewjinda A, Kumsawat P, Attakitmongcol K, Srikaew A (2008) Grape leaf disease detection from color imagery using hybrid intelligent system. In: Fifth IEEE International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp.513–516Google Scholar
  52. 52.
    Mohan KJ, Balasubramanian M, Palanivel S (2016) Detection and recognition of diseases from Paddy Plant leaf images. Int J Comput Appl 141(12):34–41Google Scholar
  53. 53.
    Mokhtar U, Ali MA, Hassenian AE, Hefny H (2015) Tomato leafs diseases detection approach based on support vector machines. In: Eleventh International IEEE, Computer Engineering Conference, pp.246–250Google Scholar
  54. 54.
    Molina JF, Gil R, Bojaca C, Gomez F, Franco H (2014) Automatic detection of early blight infection on tomato crops using a color based classification strategy. In: XIX IEEE Symposium on Image, Signal Processing and Artificial Vision, pp. 1–5Google Scholar
  55. 55.
    Mondal D, Kole DK (2015) Detection and classification technique of yellow vein mosaic virus disease in okra leaf images using leaf vein extraction and Naive Bayesian classifier. In: IEEE international conference on soft computing techniques and implementations (ICSCTI), Faridabad, 8-10 October. pp. 166–171Google Scholar
  56. 56.
    Muthukannan K, Latha P (2014) Fuzzy inference system based unhealthy region classification in plant leaf image. Int J Comput Inf Eng 8(11):2103–2107Google Scholar
  57. 57.
    Muthukannan K, Latha P (2015) A PSO model for disease pattern detection on leaf surfaces. Image Anal Stereol 34:209–216Google Scholar
  58. 58.
    Narvekar PR, Kumbhar MM, Patil SN (2014) Grape leaf diseases detection & analysis using SGDM matrix method. International Journal of Innovative Research in Computer and Communication Engineering 2(3):3365–3372Google Scholar
  59. 59.
    Orillo JW, Cruz JD, Agapito L, Satimbre PL, Valenzuela I (2014) Identification of diseases in rice plant (Oryza Sativa) using back propagation artificial neural network. In: IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, pp.1–6Google Scholar
  60. 60.
    Phadikar S, Sil J (2008) Rice disease identification using pattern recognition techniques. In: 11th international conference on computer and information technology (ICCIT 2008), Khulna, 24-27 December. pp. 420–423Google Scholar
  61. 61.
    Phadikar S, Jaya S, Das AS (2013) Rice diseases classification using feature selection and rule generation techniques. Comput Electron Agric 90:76–85CrossRefGoogle Scholar
  62. 62.
    Pixia D, Xiangdong W (2013) Recognition of greenhouse cucumber disease based on image processing technology. Open J Appl Sci 3:27–31Google Scholar
  63. 63.
    Pydipati R, Burks TF, Lee WS (2006) Identification of citrus disease using color texture features and discriminant analysis. Comput Electron Agric 52(1):49–59CrossRefGoogle Scholar
  64. 64.
    Qin F, Liu D, Sun B, Ruan L, Ma Z, Wang H (2016) Identification of alfalfa leaf diseases using image recognition technology. PLoS One 11(12):e0168274CrossRefGoogle Scholar
  65. 65.
    Rangaswami G, Mahadevan A (1998) Diseases of crop plants in India, 4th edn. PHI Learning Pvt. Ltd, New DelhiGoogle Scholar
  66. 66.
    Ratnasari EK, Mentari M, Dewi RK, Ginardi RH (2014) Sugarcane leaf disease detection and severity estimation based on segmented spots image. In: IEEE International Conference on Information, Communication Technology and System, pp.93–98Google Scholar
  67. 67.
    Revathi P, Hemalatha M (2012) Classification of cotton leaf spot diseases using image processing edge detection techniques. In: IEEE international conference on emerging trends in science, engineering and technology (INCOSET), Tiruchirappalli, 13-14 December. pp. 169–173Google Scholar
  68. 68.
    Revathi P, Hemalatha M (2014) Cotton leaf spot diseases detection utilizing feature selection with skew divergence method. International Journal of scientific engineering and technology 3(1):22–30Google Scholar
  69. 69.
    Sabrol H, Kumar S (2015) Recent studies of image and soft computing techniques for plant disease recognition and classification. Int J Comput Appl 126(1):44–55Google Scholar
  70. 70.
    Sanjaya KWV, Vijesekara HM, Wickramasinghe IM, Amalraj CR (2015) Orchid classification, disease identification and healthiness prediction system. International Journal of Scientific and Technology Research 4(3):215–220Google Scholar
  71. 71.
    Sannakki SS, Rajpurohit VS, Nargund VB, Kulkarni P (2013) Diagnosis and classification of grape leaf diseases using neural networks.In: Fourth International Conference on Computing, Communications and Networking Technologies, pp.1–5Google Scholar
  72. 72.
    Sekulska-Nalewajko J, Goclawski J (2011) A semi-automatic method for the discrimination of diseased regions in detached leaf images using fuzzy c-means clustering. In: Seventh IEEE international conference on perspective technologies and methods in MEMS design (MEMSTECH), Polyana, 11-14 May. pp. 172–175Google Scholar
  73. 73.
    Sena Jr DG, Pinto FAC, Queiroz DM, Viana PA (2003) Fall armyworm damaged maize plant identification using digital images. Biosyst Eng 85(4):449–454CrossRefGoogle Scholar
  74. 74.
    Shrivastava S, Singh SK, Hooda DS (2015) Color sensing and image processing-based automatic soybean plant foliar disease severity detection and estimation. Multimed Tools Appl 74(24):11467–11484CrossRefGoogle Scholar
  75. 75.
    Singh DV (1950) Introductory plant pathology. Trans Br Mycol Soc 33:154–160CrossRefGoogle Scholar
  76. 76.
    Deya AK, Sharmaa M, Meshram MR (2016) Image processing based leaf rot disease, detection of betel vine (Piper BetleL.). In: International conference on computational modeling and security. Procedia Comput Sci 85:748–754Google Scholar
  77. 77.
    Surendrababu V, PS C, Umapathy E (2014) Detection of rice leaf diseases using chaos and fractal dimension in image processing. International Journal on Computer Science and Engineering 6(1):69Google Scholar
  78. 78.
    Tajane V, Janwe NJ (2014) Medicinal plants disease identification using canny edge detection algorithm histogram analysis and CBIR. Int J Adv Res Comput Sci Soft Eng 4(6):530–536Google Scholar
  79. 79.
    Thresh JM (2003) The impact of plant virus diseases in developing countries. Virus and virus like diseases of major crops in developing countries. Springer, Netherlands, pp 1–30CrossRefGoogle Scholar
  80. 80.
    Tian Y, Zhao C, Lu S, Guo X (2012) SVM-based multiple classifier system for recognition of wheat leaf diseases. In: IEEE World Automation Conference, pp.189–193Google Scholar
  81. 81.
    Tucker CC, Chakraborty S (1997) Quantitative assessment of lesion characteristics and disease severity using digital image processing. J Phytopathol 145(7):273–278CrossRefGoogle Scholar
  82. 82.
    Wang H, Li G, Ma Z, Li X (2012) Image recognition of plant diseases based on principal component analysis and neural networks. In: IEEE eighth international conference on natural computation (ICNC), Chongqing, 29-31 May. pp. 246–251Google Scholar
  83. 83.
    Ward E, Foster SJ, Fraaije BA, Mccartney HA (2004) Plant pathogen diagnostics: immunological and nucleic acid-based approaches. Ann Appl Biol 145(1):1–16CrossRefGoogle Scholar
  84. 84.
    Weizheng S, Yachun W, Zhanliang C, Hongda W (2008) Grading method of leaf spot disease based on image processing. In: IEEE International Conference on Computer Science and Software Engineering 6: 491–494Google Scholar
  85. 85.
    Wu D, Xie C, Ma C (2008) The SVM classification leafminer-infected leaves based on fractal dimension. In: IEEE conference on cybernetics and intelligent systems, Chengdu, 21-24 September. pp. 147–151Google Scholar
  86. 86.
    Youwen T, Tianlai Li, Yan N (2008) The recognition of cucumber disease based on image processing and support vector machine. In: IEEE Conference on Image and Signal Processing, pp. 262–267Google Scholar
  87. 87.
    Zhang S, Zhang C (2013) Orthogonal locally discriminant projection for classification of plant leaf diseases. In: Ninth IEEE international conference on computational intelligence and security, Leshan, 14-15 December. pp. 241–245Google Scholar
  88. 88.
    Zhang M, Meng Q (2011) Automatic citrus canker detection from leaf images captured in field. Pattern Recogn Lett 32:2036–2046Google Scholar
  89. 89.
    Zhang S, Wang Z (2016) Cucumber disease recognition based on global-local singular value decomposition. Neurocomputing 205:341–348CrossRefGoogle Scholar
  90. 90.
    Zhang W, Guifa T, Chunshan W (2013) Identification of jujube trees diseases using neural network. Optik International Journal for Light and Electron Optics 124(11):1034–1037CrossRefGoogle Scholar
  91. 91.
    Zhang Z, Li Y, Wang F, He X (2014) A particle swarm optimization algorithm for neural networks in recognition of maize leaf diseases. Sensors & Transducers 166(3):181Google Scholar
  92. 92.
    Zhihua D, Huan W, Yinmao S, Yunpeng W (2013) Image segmentation method for cotton mite diseases based on color feature and area thresholding. J Theor Appl Inf Technol 48(1):527–533Google Scholar
  93. 93.
    Zhou R, Kaneko S, Tanaka F, Kayamori M, Shimizu M (2013) Matching-based Cercospora leaf spot detection in sugar beet. In: Seventh International Conference on Agricultural, Biotechnology, Biological and Biosystems Engineering, pp.204–2020Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Electronics and Communication Engineering DepartmentThapar University PatialaPatialaIndia

Personalised recommendations