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Plants Disease Identification and Classification Through Leaf Images: A Survey

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Abstract

The symptoms of plant diseases are evident in different parts of a plant; however leaves are found to be the most commonly observed part for detecting an infection. Researchers have thus attempted to automate the process of plant disease detection and classification using leaf images. Several works utilized computer vision technologies effectively and contributed a lot in this domain. This manuscript summarizes the pros and cons of all such studies to throw light on various important research aspects. A discussion on commonly studied infections and research scenario in different phases of a disease detection system is presented. The performance of state-of-the-art techniques are analyzed to identify those that seem to work well across several crops or crop categories. Discovering a set of acceptable techniques, the manuscript highlights several points of consideration along with the future research directions. The survey would help researchers to gain understanding of computer vision applications in plant disease detection.

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References

  1. Bagde S, Patil S, Patil S, Patil P (2015) Artificial neural network based plant leaf disease detection. Int J Comput Sci Mob Comput 4(4):900–905

    Google Scholar 

  2. 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, Wuhan, Hubei, December 8, pp 491–494

  3. Meunkaewjinda A, Kumsawat P, Attakitmongcol K, Srikaew A (2008) Grape leaf disease detection from color imagery using hybrid intelligent system. In: IEEE 5th international conference on electrical engineering/electronics, computer, telecommunications and information technology ECTI-CON., Krabi, pp 513–516

  4. Phadikar S, Sil J, Das AK (2013) Rice diseases classification using feature selection and rule generation techniques. Comput Electron Agric 90:76–85

    Article  Google Scholar 

  5. Rastogi A, Arora R, Sharma S (2015) Leaf disease detection and grading using computer vision technology and fuzzy logic. In: IEEE 2nd international conference on signal processing and integrated networks SPIN, pp 500–505

  6. Zhang S, Zhang C (2013) Orthogonal locally discriminant projection for classification of plant leaf diseases. In: IEEE international conference on computational intelligence and security CIS, Leshan, pp 241–245

  7. Prasad S, Peddoju SK, Ghosh D (2016) Multi-resolution mobile vision system for plant leaf disease diagnosis. Signal Image Video Process 10(2):379–388

    Article  Google Scholar 

  8. Patil JK, Kumar R (2011) Advances in image processing for detection of plant diseases. J Adv Bioinform Appl Res 2(2):135–141

    Google Scholar 

  9. Khirade SD, Patil AB (2015) Plant disease detection using image processing. In: IEEE international conference on computing communication control and automation (ICCUBEA), pp 768–771

  10. Barbedo JGA (2013) Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus 2(1):1–12

    Article  Google Scholar 

  11. Barbedo JGA (2016) A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst Eng 144:52–60

    Article  Google Scholar 

  12. Prashar K, Talwar R, Kant C (2015) A review on efficient identification of american cotton leaf diseases through training set. Int J Comput Appl 132(7):32–39

    Google Scholar 

  13. Martinelli F, Scalenghe R, Davino S et al (2015) Advanced methods of plant disease detection. A review. Agron Sustain Dev 35(1):1–25

    Article  Google Scholar 

  14. Sankaran S, Mishra A, Ehsani R, Davis C (2010) A review of advanced techniques for detecting plant diseases. Comput Electron Agric 72(1):1–13

    Article  Google Scholar 

  15. Zhang M, Meng Q (2011) Automatic citrus canker detection from leaf images captured in field. Pattern Recognit Lett 32(15):2036–2046

    Article  Google Scholar 

  16. Husin ZB, Aziz AHBA, Shakaff AYBM, Farook RBSM (2012) Feasibility study on plant chili disease detection using image processing techniques. In: IEEE 3rd international conference on intelligent system modeling and simulation ISMS., Kota Kinabalu, pp 291–296

  17. Pujari JD, Yakkundimath R, Byadgi AS (2016) SVM and ANN based classification of plant diseases using feature reduction technique. Int J Interact Multimed Artif Intell 3:6–14

    Google Scholar 

  18. Jadhav SB, Patil SB (2015) Grading of soybean leaf disease based on segmented image using k-means clustering. Int J Adv Res Electr Commun Eng 4(6):1816–1822

    Google Scholar 

  19. Phadikar S, Sil J, Das AK (2012) Classification of rice leaf diseases based on morphological changes. Int J Inf Electron Eng 2(3):460–463

    Google Scholar 

  20. Kruse OMO, 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 108:155–165

    Article  Google Scholar 

  21. Aji AF, Munajat Q, Pratama AP, Kalamullah H, Setiyawan J, Arymurthy AM (2013) Detection of palm oil leaf disease with image processing and neural network classification on mobile device. Int J Comput Theory Eng 5(3):528–532

    Article  Google Scholar 

  22. Ipm images. https://www.ipmimages.org/about/. Accessed 15 May 2017

  23. Mohanty SP, Hughes D, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1–10

    Article  Google Scholar 

  24. APS Image database. https://imagedatabase.apsnet.org/search.aspx. Accessed 16 May 2017

  25. Gharge S, Singh P (2016) Image processing for soybean disease classification and severity estimation. In: Shetty N, Prasad N, Nalini N (eds) Emerging research in computing, information, communication and applications. Springer, New Delhi, pp 493–500

    Chapter  Google Scholar 

  26. University of Minnesota Extension. https://www.extension.umn.edu/. Accessed 16 May 2017

  27. Sanyal P, Bhattacharya U, Parui SK, Bandyopadhyay SK, Patel S (2007) Color texture analysis of rice leaves diagnosing deficiency in the balance of mineral levels towards improvement of crop productivity In: IEEE 10th international conference on information technology ICIT, Orissa, pp 85–90

  28. Camargo A, Smith JS (2009) An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosyst Eng 102(1):9–21

    Article  Google Scholar 

  29. Pujari JD, Yakkundimath RS, Jahagirdar S, Byadgi AM (2015) Quantitative detection of soybean rust using image processing techniques. J Crop Prot 5(1):75–87

    Article  Google Scholar 

  30. Rumpf T, Mahlein AK, Steiner U, Oerke EC, Dehne HW, Plümer L (2010) Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput Electron Agric 74(1):91–99

    Article  Google Scholar 

  31. Pires RDL, Gonçalves DN, Oruê JPM, Kanashiro WES, Rodrigues JF, Machado BB, Gonçalves WN (2016) Local descriptors for soybean disease recognition. Comput Electron Agric 125:48–55

    Article  Google Scholar 

  32. Barbedo JGA (2014) An automatic method to detect and measure leaf disease symptoms using digital image processing. Plant Dis 98(12):1709–1716

    Article  Google Scholar 

  33. Anthonys G, Wickramarachchi N (2009) An image recognition system for crop disease identification of paddy fields in Sri Lanka. In: IEEE international conference on industrial and information systems ICIIS, Sri Lanka, pp 403–407

  34. Pydipati R, Burks TF, Lee WS (2006) Identification of citrus disease using color texture features and discriminant analysis. Comput Electron Agric 52(1):49–59

    Article  Google Scholar 

  35. Bauer SD, Korč F, Förstner W (2011) The potential of automatic methods of classification to identify leaf diseases from multispectral images. Precis Agric 12(3):361–377

    Article  Google Scholar 

  36. Xu G, Zhang F, Shah SG, Ye Y, Mao H (2011) Use of leaf color images to identify nitrogen and potassium deficient tomatoes. Pattern Recognit Lett 32(11):1584–1590

    Article  Google Scholar 

  37. Yao Q, Guan Z, Zhou Y, Tang J, Hu Y, Yang B (2009) Application of support vector machine for detecting rice diseases using shape and color texture features. In: IEEE international conference on engineering computation ICEC, Hong Kong, pp 79–83

  38. Mirik M, Michels GJ, Kassymzhanova-Mirik S, Elliott NC, Catana V, Jones DB, Bowling R (2006) Using digital image analysis and spectral reflectance data to quantify damage by greenbug (Hemitera: Aphididae) in winter wheat. Comput Electron Agric 51(1):86–98

    Article  Google Scholar 

  39. Huang KY (2007) Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features. Comput Electron Agric 57(1):3–11

    Article  Google Scholar 

  40. Oberti R, Marchi M, Tirelli P, Calcante A, Iriti M, Borghese AN (2014) Automatic detection of powdery mildew on grapevine leaves by image analysis: optimal view-angle range to increase the sensitivity. Comput Electron Agric 104:1–8

    Article  Google Scholar 

  41. Shrivastava S, Hooda DS (2014) Automatic brown spot and frog eye detection from the image aptured in the field. Am J Intell Syst 4(4):131–134

    Google Scholar 

  42. Cui D, Zhang Q, Li M, Zhao Y, Hartman GL (2009) Detection of soybean rust using a multispectral image sensor. Sens Instrum Food Qual Saf 3(1):49–56

    Article  Google Scholar 

  43. Lu J, Cui D, Jiang H (2013) Discrimination of tomato yellow leaf curl disease using hyperspectral imaging. American Society of Agricultural and Biological Engineers, Kansas City, Missouri, July 21-July 24, p 1

  44. Kumbhar NP, Dhaygude SB (2013) Crop disease detection using CBIR. Int J Emerg Technol Comput Appl Sci 3(2):113–118

    Google Scholar 

  45. Asfarian A, Herdiyeni Y, Rauf A, Mutaqin KM (2013) Paddy diseases identification with texture analysis using fractal descriptors based on Fourier spectrum. In: IEEE international conference on computer, control, informatics and its applications IC3INA, Jakarta, pp 77–81

  46. Sannakki SS, Rajpurohit VS, Nargund VB, Kulkarni P (2013) Diagnosis and classification of grape leaf diseases using neural networks. In: IEEE 4th international conference on computing, communications and networking technologies ICCCNT, Tiruchengode, pp 1–5

  47. Wang L, Dong F, Guo Q, Nie C, Sun S (2014) Improved rotational kernel transformation directional feature for recognition of wheat stripe rust and powdery mildew. In: IEEE 7th international conference on image and signal processing CISP, Dalian, pp 286–291

  48. Zhang SW, Shang YJ, Wang L (2015) Plant disease recognition based on plant leaf image. J Anim Plant Sci 25(Suppl. 1):42–45

    Google Scholar 

  49. Arivazhagan S, Shebiah RN, Ananthi S, Varthini SV (2013) Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int CIGR J 15(1):211–217

    Google Scholar 

  50. Bashish AD, Braik M, Bani-Ahmed S (2010) A framework for detection and classification of plant leaf and stem diseases. In: IEEE international conference on signal and image processing ICSIP, Chennai, pp 113–118

  51. Majumdar D, Kole DK, Chakraborty A, Majumdar DD (2015) An integrated digital image analysis system for detection, recognition and diagnosis of disease in wheat leaves. In: 3rd international ACM internat. Symposium on women in computing and informatics, New York, pp 400–405

  52. Wiwart M, Fordoński G, Żuk-Gołaszewska K, Suchowilska E (2009) Early diagnostics of macronutrient deficiencies in three legume species by color image analysis. Comput Electron Agric 65(1):125–132

    Article  Google Scholar 

  53. Kai S, Zhikun L, Hang S, Chunhong G (2011) A research of maize disease image recognition of corn based on BP networks. In: proceedings of third international confeence on measuring technology and mechatronics automation ICMTMA. IEEE, Shanghai, pp 246–249

  54. Molina JF, Gil R, Bojacá C, Gómez F, Franco H (2014) Automatic detection of early blight infection on tomato crops using a color based classification strategy. In: IEEE XIX symposium on image, signal processing and artificial vision, September, pp 1–5

  55. Wang H, Li G, Ma Z, Li X (2012) Application of neural networks to image recognition of plant diseases. In: IEEE international conference on systems and informatics ICSAI, Yantai, pp 2159–2164

  56. Youwen T, Tianlai L, Yan N (2008) The recognition of cucumber disease based on image processing and support vector machine. In: IEEE congress on image and signal processing CISP’08, May, vol 2, pp 262–267

  57. Shinde RC, Mathew CJ, Patil CY (2015) Segmentation technique for soybean leaves disease detection. Int J Adv Res 3(5):522–528

    Google Scholar 

  58. Hairuddin MA, Tahir NM, Baki SRS (2011) Overview of image processing approach for nutrient deficiencies detection in elaeis guineensis. In: IEEE international conference on system engineering and technology ICSET, Shah Alam, pp 116–120

  59. Bandi SR, Varadharajan A, Chinnasamy A (2013) Performance evaluation of various statistical classifiers in detecting the diseased citrus leaves. Int J Eng Sci Technol 5(2):298–307

    Google Scholar 

  60. Xia C, Lee JM, Li Y, Song YH, Chung BK, Chon TS (2013) Plant leaf detection using modified active shape models. Biosyst Eng 116(1):23–35

    Article  Google Scholar 

  61. Bankar S, Dube A, Kadam P, Deokule S (2014) Plant disease detection techniques using canny edge detection & color histogram in image processing. Int J Comput Sci Inf Technol 52(2):1165–1168

    Google Scholar 

  62. Singh V, Misra AK (2015) Detection of unhealthy region of plant leaves using image processing and genetic algorithm. In: IEEE international conference on advances in computer engineering and applications ICACEA, pp 1028–1032

  63. Pujari JD, Yakkundimath R, Byadgi AS (2015) Image processing based detection of fungal diseases in plants. Proc Comput Sci 46:1802–1808

    Article  Google Scholar 

  64. Danping W, Botao W, Yue Y (2013) The identification of powdery mildew spores image based on the integration of intelligent spore image sequence capture device. In: IEEE 9th international conference on intelligent information hiding and multimedia signal processing IIH-MSP, Beijing, pp 170–180

  65. Phadikar S, Sil J (2008) Rice disease identification using pattern recognition techniques. In: IEEE 11th international conference on computer and information technology ICCIT, Khulna, pp 420–423

  66. Masood R, Khan SA, Khan MNA (2016) Plants disease segmentation using image processing. Int J Mod Educ Comput Sci 8(1):24–32

    Article  Google Scholar 

  67. Cui D, Zhang Q, Li M, Hartman GL, Zhao Y (2010) Image processing methods for quantitatively detecting soybean rust from multispectral images. Biosyst Eng 107(3):186–193

    Article  Google Scholar 

  68. Pang J, Bai ZY, Lai JC, Li SK (2011) Automatic segmentation of crop leaf spot disease images by integrating local threshold and seeded region growing. In: IEEE international conference on image analysis and signal processing, October, pp 590–594

  69. Shire A, Jawarkar U, Manmode MM (2015) A review paper on: agricultural plant leaf disease detection using image processing. Int J Innov Sci Eng Technol 2(1):282–285

    Google Scholar 

  70. Gui J, Hao L, Zhang Q, Bao X (2015) A new method for soybean leaf disease detection based on modified salient regions. Int J Multim Ubiquitous Eng 10:45–52

    Article  Google Scholar 

  71. Krishnan M, Sumithra MG (2013) A novel algorithm for detecting bacterial leaf scorch of shade trees using image processing. In: IEEE Malaysia international conference on communications MICC, Kuala Lumpur, pp 474–478

  72. Kurniawati NN, Abdullah SNHS, Abdullah S, Abdullah S (2009) Investigation on image processing techniques for diagnosing paddy diseases. In: IEEE international conference on soft computing and pattern recognition SOCPAR., Malacca, pp 272–277

  73. Gavhale KR, Gawande U (2014) An overview of the research on plant leaves disease detection using image processing techniques. IOSR J Comput Eng 16(1):10–16

    Article  Google Scholar 

  74. Shrivastava S, Singh SK, Hooda DS (2014) Statistical texture and normalized discrete cosine transform-based automatic soya plant foliar infection cataloguing. Br J Math Comput Sci 4(20):2901–2916

    Article  Google Scholar 

  75. Gurjar AA, Gulhane VA (2012) Disease detection on cotton leaves by eigenfeature regularization and extraction technique. Int J Electr Commun Soft Comput Sci Eng 1(1):1–4

    Google Scholar 

  76. Mokhtar U, El Bendary N, Hassenian AE, Emary E, Mahmoud MA, Hefny H, Tolba MF (2015) SVM-based detection of tomato leaves diseases. In intelligent systems. Springer International Publishing, Berlin, pp 641–652

    Google Scholar 

  77. Machine Learning. https://en.wikipedia.org/wiki/Machine_learning. Accessed 17 May 2017

  78. Ghaiwat SN, Arora P (2015) Cotton leaf disease detection by feature extraction. In: Research advances in the integration of big data and smart computing, Oct 13:89

  79. Camargo A, Smith JS (2009) Image pattern classification for the identification of disease causing agents in plants. Comput Electron Agric 66(2):121–125

    Article  Google Scholar 

  80. Rothe PR, Kshirsagar RV (2015) Cotton leaf disease identification using pattern recognition techniques. In: IEEE international conference on pervasive computing (ICPC), January, pp 1–6

  81. Ramakrishnan M (2015) Groundnut leaf disease detection and classification by using back probagation algorithm. In: IEEE international conference on communications and signal processing (ICCSP), April, pp 0964–0968

  82. Barbedo JGA (2013) Automatically measuring early and late leaf spot lesions in peanut plants using digital image processing. In: Congresso brasileiro de agroinformática vol. 9

  83. Nisale SS, Bharambe CJ, More VN (2011) Detection and analysis of deficiencies in groundnut plant using geometric moments. World Acad Sci Eng Technol 5:608–612

    Google Scholar 

  84. Rangel BM, Fernández MA, Murillo JC, Ortega JC, Arreguín JM (2016) KNN-based image segmentation for grapevine potassium deficiency diagnosis. In: IEEE international conference on electronics, communications and computers (CONIELECOMP) Feb 24, pp 48–53

  85. Kole DK, Ghosh A, Mitra S (2014) Detection of downy mildew disease present in the grape leaves based on fuzzy set theory. In: Advanced computing, networking and informatics, Volume 1, pp 377–384, Springer International Publishing

  86. Jian Z, Wei Z (2010) Support vector machine for recognition of cucumber leaf diseases. In: IEEE international conference on advanced computer control, ICACC, March, vol 5, pp 264–266

  87. Asefpour Vakilian K, Massah J (2013) An artificial neural network approach to identify fungal diseases of cucumber (Cucumis sativus L.) plants using digital image processing. Arch Phytopathol Plant Prot 46(13):1580–1588

    Article  Google Scholar 

  88. Yun S, Xianfeng W, Shanwen Z, Chuanlei Z (2015) PNN based crop disease recognition with leaf image features and meteorological data. Int J Agric Biol Eng 8(4):60–68

    Google Scholar 

  89. Sabrol H, Kumar S (2016) Intensity based feature extraction for tomato plant disease recognition by classification using decision tree. Int J Comput Sci Inf Secur 14(9):622–626

    Google Scholar 

  90. Sabrol H, Kumar S (2016) Fuzzy and neural network based tomato plant disease classification using natural outdoor images. Indian J Sci Technol 9(44):1–8

    Article  Google Scholar 

  91. Samanta D, Ghosh A (2012) Histogram approach for detection of maize leaf damage. Int J Comput Sci Telecommun 3(2):26–28

    Google Scholar 

  92. Luo J, Geng S, Xiu C, Song D, Dong T (2015) A curvelet-SC recognition method for maize disease. J Electr Comput Eng 2015:25

    Google Scholar 

  93. Barbedo JGA, Godoy CV (2015) Automatic classification of soybean diseases based on digital images of leaf symptoms. SBI AGRO

  94. 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):11467–11484

    Article  Google Scholar 

  95. Dandawate Y, Kokare R (2015) An automated approach for classification of plant diseases towards development of futuristic Decision Support System in Indian perspective. In: IEEE international conference on advances in computing, communications and informatics (ICACCI), August, pp 794–799

  96. Mohan KJ, Balasubramanian M, Palanivel S (2016) Detection and recognition of diseases from paddy plant leaf images. Int J Comput Appl 144(12):34–41

    Google Scholar 

  97. Verma T, Satpathy SK, Sharma LK (2011) A step towards precision farming of rice crop by estimating loss caused by leaf blast disease using digital image processing and fuzzy clustering. Int J Comput Trends Technol pp 1–6

  98. Majumdar D, Ghosh A, Kole DK, Chakraborty A, Majumder DD (2014) Application of fuzzy c-means clustering method to classify wheat leaf images based on the presence of rust disease. In: International conference on frontiers of intelligent computing: theory and applications (FICTA), Springer International Publishing, pp 277–284

  99. Cai X, Sun Y, Zhao Y, Damerow L, Lammers PS, Sun W, Tang Y (2013) Smart detection of leaf wilting by 3D image processing and 2D Fourier transform. Comput Electron Agric 90:68–75

    Article  Google Scholar 

  100. Schikora M, Neupane B, Madhogaria S, Koch W, Cremers D, Hirt H, Kogel KH, Schikora A (2012) An image classification approach to analyze the suppression of plant immunity by the human pathogen Salmonella Typhimurium. BMC Bioinform 13(1):171

    Article  Google Scholar 

  101. Barbedo JGA, Koenigkan LV, Santos TT (2016) Identifying multiple plant diseases using digital image processing. Biosyst Eng 147:104–116

    Article  Google Scholar 

  102. Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosci pp 1–11

  103. Bock CH, Poole GH, Parker PE, Gottwald TR (2010) Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. CRC Crit Rev Plant Sci 29(2):59–107

    Article  Google Scholar 

  104. Pethybridge SJ, Nelson SC (2015) Leaf Doctor: a new portable application for quantifying plant disease severity. Plant Dis 99(10):1310–1316

    Article  Google Scholar 

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  1. Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India

    Sukhvir Kaur & Shreelekha Pandey

  2. Bennett University, Greater Noida, U.P, India

    Shivani Goel

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  1. Sukhvir Kaur
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Kaur, S., Pandey, S. & Goel, S. Plants Disease Identification and Classification Through Leaf Images: A Survey. Arch Computat Methods Eng 26, 507–530 (2019). https://doi.org/10.1007/s11831-018-9255-6

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