Skip to main content

Advertisement

Log in

Applications of Computer Vision in Plant Pathology: A Survey

  • Original Paper
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

The real-time decision support system can enhance the crop or plant growth, therefore, increasing their productivity, quality, and economic value. This also helps us in serving the nature by supervising the plant growth in balancing the environment. Computer vision techniques have proven to play an important role in the number of applications like medical, defense, agriculture, remote sensing, business analysis, etc. The use of digital image processing methods for simulating the visual capability of the human being has proven to be a dynamic feature in smart or precision agriculture. This concept has provided with the automatic preventing and monitoring of plants, cultivation, disease management, water management etc. to increase the crop productivity and quality. In this paper, we have surveyed the number of articles that adopt the concept of computer vision and soft computing methods for the identification and classification of diseases from the leaf of the plant. Our aim is to present the state of the art of the concepts, applications, and theories associated with the digital image processing and soft computing methodologies. The various outcomes have been discussed separately.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Wang Z et al (2017) Review of plant identification based on image processing. Arch Computat Methods Eng 24:637–654. https://doi.org/10.1007/s11831-016-9181-4

    Article  MathSciNet  MATH  Google Scholar 

  2. Arribas JI et al (2011) Leaf classification in sunflower crops by computer vision and neural networks. Comput Electron Agric 78:9–18. https://doi.org/10.1016/j.compag.2011.05.007

    Article  Google Scholar 

  3. Barth R et al (2018) Data synthesis methods for semantic segmentation in agriculture: a Capsicum annuum dataset. Comput Electron Agric 144:284–296. https://doi.org/10.1016/j.compag.2017.12.001

    Article  Google Scholar 

  4. Chouhan SS et al (2018) Image segmentation using computational intelligence techniques: review. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-018-9257-4

    Article  Google Scholar 

  5. Barth R et al (2017) Synthetic bootstrapping of convolutional neural networks for semantic plant part segmentation. Comput Electron Agric. https://doi.org/10.1016/j.compag.2017.11.040

    Article  Google Scholar 

  6. Bhange M, Hingoliwala HA (2015) Smart farming: pomegranate disease detection using image processing. In: Second international symposium on computer vision and the internet (VisionNet’15), Procedia Computer Science, vol 58, pp 280–288. https://doi.org/10.1016/j.procs.2015.08.022

    Article  Google Scholar 

  7. dos Santos Ferreira A et al (2017) Weed detection in soybean crops using ConvNets. Comput Electron Agric 143:314–324. https://doi.org/10.1016/j.compag.2017.10.027

    Article  Google Scholar 

  8. Cope JS et al (2012) Plant species identification using digital morphometrics: a review. Expert Syst Appl 39:7562–7573. https://doi.org/10.1016/j.eswa.2012.01.073

    Article  Google Scholar 

  9. Barbedo JGA (2016) A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst Eng 144:52–60. https://doi.org/10.1016/j.biosystemseng.2016.01.017

    Article  Google Scholar 

  10. Huang Y et al (2010) Development of soft computing and applications in agricultural and biological engineering. Comput Electron Agric 71:107–127. https://doi.org/10.1016/j.compag.2010.01.001

    Article  Google Scholar 

  11. Camargo A, Smith JS (2009) An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosyst Eng 102:9–21. https://doi.org/10.1016/j.biosystemseng.2008.09.030

    Article  Google Scholar 

  12. Hassanien AE et al (2017) An improved moth flame optimization algorithm based on rough sets for tomato diseases detection. Comput Electron Agric 136:86–96. https://doi.org/10.1016/j.compag.2017.02.026

    Article  Google Scholar 

  13. Johannes A et al (2017) Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Comput Electron Agric 138:200–209. https://doi.org/10.1016/j.compag.2017.04.013

    Article  Google Scholar 

  14. Dey AK et al (2016) Image processing based leaf rot disease, detection of betel vine (Piper BetleL.). In: International conference on computational modeling and security (CMS 2016), Procedia Computer Science, vol 85, pp 748–754. https://doi.org/10.1016/j.procs.2016.05.262

    Article  Google Scholar 

  15. Anand R et al (2016) An application of image processing techniques for detection of diseases on brinjal leaves using K-means clustering method. In: 2016 international conference on recent trends in information technology (ICRTIT), Chennai, pp 1–6. https://doi.org/10.1109/icrtit.2016.7569531

  16. Dandawate Y, Kokare R (2015) An automated approach for classification of plant diseases towards development of futuristic decision support system in Indian perspective. In: 2015 International conference on advances in computing, communications and informatics (ICACCI), Kochi, pp 794–799. https://doi.org/10.1109/icacci.2015.7275707

  17. Singh A et al (2016) Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci 21(2):110–124. https://doi.org/10.1016/j.tplants.2015.10.015

    Article  Google Scholar 

  18. Chouhan SS et al (2018) Bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases: an automatic approach towards plant pathology. IEEE Access. https://doi.org/10.1109/access.2018.2800685

    Article  Google Scholar 

  19. 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 125:99–112. https://doi.org/10.1016/j.compag.2016.04.033

    Article  Google Scholar 

  20. Clement A et al (2015) A new colour vision system to quantify automatically foliar discolouration caused by insect pests feeding on leaf cells. Biosyst Eng 133:128–140. https://doi.org/10.1016/j.biosystemseng.2015.03.007

    Article  Google Scholar 

  21. Biswas S et al (2014) Severity identification of potato late blight disease from crop images captured under uncontrolled environment. In: 2014 IEEE Canada international humanitarian technology conference—(IHTC), Montreal, QC, pp 1–5. https://doi.org/10.1109/ihtc.2014.7147519

  22. Dhaware CG, Wanjale KH (2017) Modern approach for plant leaf disease classification which depends on leaf image processing. In: 2017 international conference on computer communication and informatics (ICCCI), Coimbatore, pp 1–4. https://doi.org/10.1109/iccci.2017.8117733

  23. Tetila EC et al (2017) Identification of soybean foliar diseases using unmanned aerial vehicle images. IEEE Geosci Remote Sens Lett 14(12):2190–2194. https://doi.org/10.1109/lgrs.2017.2743715

    Article  Google Scholar 

  24. Dhakate M, Ingole AB (2015) Diagnosis of pomegranate plant diseases using neural network. In: 2015 fifth national conference on computer vision, pattern recognition, image processing and graphics (NCVPRIPG), Patna, pp 1–4. https://doi.org/10.1109/ncvpripg.2015.7490056

  25. Mondal D et al (2017) Gradation of yellow mosaic virus disease of okra and bitter gourd based on entropy based binning and Naive Bayes classifier after identification of leaves. Comput Electron Agric 142:485–493. https://doi.org/10.1016/j.compag.2017.11.024

    Article  Google Scholar 

  26. Cui D et al (2009) Detection of soybean rust using a multispectral image sensor. Sens Instrum Food Qual 3:49–56. https://doi.org/10.1007/s11694-009-9070-8

    Article  Google Scholar 

  27. Williams D et al (2017) A method for automatic segmentation and splitting of hyperspectral images of raspberry plants collected in field conditions. Plant Methods. https://doi.org/10.1186/s13007-017-0226-y

    Article  Google Scholar 

  28. Kiani E, Mamedov T (2017) Identification of plant disease infection using soft-computing: application to modern botany. In: 9th international conference on theory and application of soft computing, computing with words and perception, ICSCCW. https://doi.org/10.1016/j.procs.2017.11.323

    Article  Google Scholar 

  29. Aksoy EE et al (2015) Modeling leaf growth of rosette plants using infrared stereo image Sequences. Comput Electron Agric 110:78–90. https://doi.org/10.1016/j.compag.2014.10.020

    Article  Google Scholar 

  30. Hamuda E et al (2016) A survey of image processing techniques for plant extraction and segmentation in the field. Comput Electron Agric 125:184–199. https://doi.org/10.1016/j.compag.2016.04.024

    Article  Google Scholar 

  31. Ndo EGD et al (2010) Altitude, tree species and soil type are the main factors influencing the severity of Phaeoramularia leaf and fruit spot disease of citrus in the humid zones of Cameroon. Eur J Plant Pathol 128:385–397. https://doi.org/10.1007/s10658-010-9660-7

    Article  Google Scholar 

  32. Martinelli F et al (2015) Advanced methods of plant disease detection. A review. Agron Sustain Dev 35:1–25. https://doi.org/10.1007/s13593-014-0246-1

    Article  Google Scholar 

  33. Francis J et al (2016) Identification of leaf diseases in pepper plants using soft computing techniques. In: 2016 conference on emerging devices and smart systems (ICEDSS), Namakkal, pp 168–173. https://doi.org/10.1109/icedss.2016.7587787

  34. Ganesan P et al (2017) CIELuv color space for identification and segmentation of disease affected plant leaves using fuzzy based approach. In: 2017 third international conference on science technology engineering and management (ICONSTEM), Chennai, pp 889–894. https://doi.org/10.1109/iconstem.2017.8261330

  35. Dhingra G et al (2018) Study of digital image processing techniques for leaf disease detection and classification. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-5445-8

    Article  Google Scholar 

  36. Ali H et al (2017) Symptom based automated detection of citrus diseases using color histogram and textural descriptors. Comput Electron Agric 138:92–104. https://doi.org/10.1016/j.compag.2017.04.008

    Article  Google Scholar 

  37. Asraf HM et al (2012) A comparative study in kernel-based support vector machine of oil palm leaves nutrient disease. In: International symposium on robotics and intelligent sensors 2012 (IRIS 2012), Procedia Engineering, vol 41, pp 1353–1359. https://doi.org/10.1016/j.proeng.2012.07.321

    Article  Google Scholar 

  38. Scharr H et al (2016) Leaf segmentation in plant phenotyping: a collation study. Mach Vis Appl 27:585–606. https://doi.org/10.1007/s00138-015-0737-3

    Article  Google Scholar 

  39. Islam M et al (2017) Detection of potato diseases using image segmentation and multiclass support vector machine. In: 2017 IEEE 30th Canadian conference on electrical and computer engineering (CCECE), Windsor, ON, pp 1–4. https://doi.org/10.1109/ccece.2017.7946594

  40. Behmann J et al (2014) Detection of early plant stress responses in hyperspectral images. ISPRS J Photogramm Remote Sens 93:98–111. https://doi.org/10.1016/j.isprsjprs.2014.03.016

    Article  Google Scholar 

  41. Waldchen J, Mader P (2018) Plant species identification using computer vision techniques: a systematic literature review. Arch Computat Methods Eng. https://doi.org/10.1007/s11831-016-9206-z

    Article  MathSciNet  MATH  Google Scholar 

  42. Barbedo JGA et al (2016) Identifying multiple plant diseases using digital image processing. Biosyst Eng 147:104–116. https://doi.org/10.1016/j.biosystemseng.2016.03.012

    Article  Google Scholar 

  43. Barbedo JGA (2013) Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus. https://doi.org/10.1186/2193-1801-2-660

    Article  Google Scholar 

  44. Guyot J et al (2013) Segmentation applied to weather-disease relationships in South American leaf blight of the rubber tree. Eur J Plant Pathol 126:349–362. https://doi.org/10.1007/s10658-009-9540-1

    Article  Google Scholar 

  45. Zhang J et al (2017) Discrimination of winter wheat disease and insect stresses using continuous wavelet features extracted from foliar spectral measurements. Biosyst Eng 162:20–29. https://doi.org/10.1016/j.biosystemseng.2017.07.003

    Article  Google Scholar 

  46. Barbedo JGA (2017) A new automatic method for disease symptom segmentation in digital photographs of plant leaves. Eur J Plant Pathol 147:349–364. https://doi.org/10.1007/s10658-016-1007-6

    Article  Google Scholar 

  47. Ubbens J et al (2018) The use of plant models in deep learning: an application to leaf counting in rosette plants. Plant Methods. https://doi.org/10.1186/s13007-018-0273-z

    Article  Google Scholar 

  48. Ma J et al (2017) A segmentation method for greenhouse vegetable foliar disease spots images using color information and region growing. Comput Electron Agric 142:110–117. https://doi.org/10.1016/j.compag.2017.08.023

    Article  Google Scholar 

  49. Kaur R, Kang SS (2015) An enhancement in classifier support vector machine to improve plant disease detection. In: 2015 IEEE 3rd international conference on MOOCs, innovation and technology in education (MITE), Amritsar, pp 135–140. https://doi.org/10.1109/mite.2015.7375303

  50. Khirade SD, Patil AB (2015) Plant disease detection using image processing. In: 2015 international conference on computing communication control and automation. https://doi.org/10.1109/iccubea.2015.153

  51. Journaux L et al (2011) Plant leaf roughness analysis by texture classification with generalized Fourier descriptors in a dimensionality reduction context. Precis Agric 12:345–360. https://doi.org/10.1007/s11119-010-9208-z

    Article  Google Scholar 

  52. Zhang L et al (2016) Individual leaf identification from horticultural crop images based on the leaf skeleton. Comput Electron Agric 127:184–196. https://doi.org/10.1016/j.compag.2016.06.017

    Article  Google Scholar 

  53. Ali-Shtayeh MS et al (2014) Squash leaf curl virus (SLCV): a serious disease threatening cucurbits production in Palestine. Virus Genes 48:320–328. https://doi.org/10.1007/s11262-013-1012-1

    Article  Google Scholar 

  54. Schikora M, Schikora A (2014) Image-based analysis to study plant infection with human pathogens. Computat Struct Biotechnol J 12:1–6. https://doi.org/10.1016/j.csbj.2014.09.010

    Article  Google Scholar 

  55. Solahudin M et al (2015) Gemini virus attack analysis in field of chili (Capsicum annuum L.) using aerial photography and Bayesian segmentation method. In: The 1st international symposium on LAPAN-IPB satellite for food security and environmental monitoring, Procedia Environmental Sciences, vol 24, pp 254–257. https://doi.org/10.1016/j.proenv.2015.03.033

    Article  Google Scholar 

  56. Jamil N et al (2015) Automatic plant identification: is shape the key feature? In: 2015 IEEE international symposium on robotics and intelligent sensors (IRIS 2015), Procedia Computer Science, vol 76, pp 436–442. https://doi.org/10.1016/j.procs.2015.12.287

    Article  Google Scholar 

  57. Kruse OMO et al (2014) Pixel classification methods for identifying and quantifying leaf surface injury from digital images. Comput Electron Agric 108:155–165. https://doi.org/10.1016/j.compag.2014.07.010

    Article  Google Scholar 

  58. Padol PB, Sawant SD (2016) Fusion classification technique used to detect downy and powdery mildew grape leaf diseases. In: 2016 international conference on global trends in signal processing, information computing and communication (ICGTSPICC), Jalgaon, pp 298–301. https://doi.org/10.1109/icgtspicc.2016.7955315

  59. Prasad S et al (2014) Energy efficient mobile vision system for plant leaf disease identification. In: 2014 IEEE wireless communications and networking conference (WCNC), Istanbul, 2014, pp 3314–3319. https://doi.org/10.1109/wcnc.2014.6953083

  60. Padol PB, Yadav AA (2016) SVM classifier based grape leaf disease detection. In: 2016 conference on advances in signal processing (CASP), Pune, pp 175–179. https://doi.org/10.1109/casp.2016.7746160

  61. Parikh A et al (2016) Disease detection and severity estimation in cotton plant from unconstrained images. In: 2016 IEEE international conference on data science and advanced analytics. https://doi.org/10.1109/dsaa.2016.81

  62. Phadikar S, Goswami J (2016) Vegetation indices based segmentation for automatic classification of brown spot and blast diseases of rice. In: 2016 3rd international conference on recent advances in information technology (RAIT), Dhanbad, pp 284–289. https://doi.org/10.1109/rait.2016.7507917

  63. Prajapati BS et al (2016) A survey on detection and classification of cotton leaf diseases. In: 2016 international conference on electrical, electronics, and optimization techniques (ICEEOT), Chennai, pp 2499–2506. https://doi.org/10.1109/iceeot.2016.7755143

  64. Prasad S et al (2014) Mobile mixed reality based damage level estimation of diseased plant leaf. In: 2014 eighth international conference on next generation mobile applications, services and technologies. https://doi.org/10.1109/ngmast.2014.56

  65. Mishra P et al (2017) Close range hyperspectral imaging of plants: a review. Biosyst Eng. https://doi.org/10.1016/j.biosystemseng.2017.09.009

    Article  Google Scholar 

  66. Qiu-xia H et al (2017) Wheat leaf lesion color image segmentation with improved multichannel selection based on the Chan-Vese model. Comput Electron Agric 135:260–268. https://doi.org/10.1016/j.compag.2017.01.016

    Article  Google Scholar 

  67. Narmadha RP, Arulvadivu G (2017) Detection and measurement of paddy leaf disease symptoms using image processing. In: 2017 international conference on computer communication and informatics (ICCCI), Coimbatore, pp 1–4. https://doi.org/10.1109/iccci.2017.8117730

  68. Ramakrishnan M, Sahaya Anselin Nisha A (2015) Groundnut leaf disease detection and classification by using back probagation algorithm. In: 2015 international conference on communications and signal processing (ICCSP), Melmaruvathur, pp 0964–0968. https://doi.org/10.1109/iccsp.2015.7322641

  69. Rastogi A et al (2015) Leaf disease detection and grading using computer vision technology & fuzzy logic. In: 2015 2nd international conference on signal processing and integrated networks (SPIN), Noida, pp 500–505. https://doi.org/10.1109/spin.2015.7095350

  70. Reza ZN et al (2016) Detecting jute plant disease using image processing and machine learning. In: 2016 3rd international conference on electrical engineering and information communication technology (ICEEICT), Dhaka, pp 1–6. https://doi.org/10.1109/ceeict.2016.7873147

  71. Pires RDL et al (2015) Local descriptors for soybean disease recognition. Comput Electron Agric 125:48–55. https://doi.org/10.1016/j.compag.2016.04.032

    Article  Google Scholar 

  72. Zhou R et al (2014) Disease detection of Cercospora Leaf Spot in sugar beet by robust template matching. Comput Electron Agric 108:58–70. https://doi.org/10.1016/j.compag.2014.07.004

    Article  Google Scholar 

  73. Rothe PR, Kshirsagar RV (2015) Cotton leaf disease identification using pattern recognition techniques. In: 2015 international conference on pervasive computing (ICPC), Pune, pp 1–6. https://doi.org/10.1109/pervasive.2015.7086983

  74. Nandhini SA et al (2018) Web enabled plant disease detection system for agricultural applications using WMSN. Wireless Pers Commun. https://doi.org/10.1007/s11277-017-5092-4

    Article  Google Scholar 

  75. Sabrol H, Satish K (2016) Tomato plant disease classification in digital images using classification tree. In: 2016 international conference on communication and signal processing (ICCSP), Melmaruvathur, pp 1242–1246. https://doi.org/10.1109/iccsp.2016.7754351

  76. Sarangi S et al (2016) Automation of agriculture support systems using Wisekar: case study of a crop-disease advisory service. Comput Electron Agric 122:200–210. https://doi.org/10.1016/j.compag.2016.01.009

    Article  Google Scholar 

  77. Sannakki SS et al (2013) Diagnosis and classification of grape leaf diseases using neural networks. In: 2013 fourth international conference on computing, communications and networking technologies (ICCCNT), Tiruchengode, pp 1–5. https://doi.org/10.1109/icccnt.2013.6726616

  78. Raza S-e et al (2015) Registration of thermal and visible light images of diseased plants using silhouette extraction in the wavelet domain. Pattern Recognit 48:2119–2128. https://doi.org/10.1016/j.patcog.2015.01.027

    Article  Google Scholar 

  79. Sarkar RK, Pramanik A (2015) Segmentation of plant disease spots using automatic SRG algorithm: a look up table approach. In: 2015 international conference on advances in computer engineering and applications, Ghaziabad, pp 1–5. https://doi.org/10.1109/icacea.2015.7194375

  80. Zhang S et al (2017) Fusion of superpixel, expectation maximization and PHOG for recognizing cucumber diseases. Comput Electron Agric 140:338–347. https://doi.org/10.1016/j.compag.2017.06.016

    Article  Google Scholar 

  81. Zhang S et al (2018) Plant diseased leaf segmentation and recognition by fusion ofsuperpixel, K-means and PHOG. Optik 157:866–872. https://doi.org/10.1016/j.ijleo.2017.11.190

    Article  Google Scholar 

  82. Zhang S et al (2017) Plant disease leaf image segmentation based on superpixel clustering and EM algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3067-8

    Article  Google Scholar 

  83. Prasad S et al (2016) Multi-resolution mobile vision system for plant leaf disease diagnosis. SIViP 10:379–388. https://doi.org/10.1007/s11760-015-0751-y

    Article  Google Scholar 

  84. Prasad S et al (2017) An efficient low vision plant leaf shape identification system for smart phones. Multimed Tools Appl 76:6915–6939. https://doi.org/10.1007/s11042-016-3309-2

    Article  Google Scholar 

  85. Singh V et al (2015) Detection of unhealthy region of plant leaves using image processing and genetic algorithm. In: 2015 international conference on advances in computer engineering and applications, Ghaziabad, pp 1028–1032. https://doi.org/10.1109/icacea.2015.7164858

  86. Soni P, Chahar R (2016) A segmentation improved robust PNN model for disease identification in different leaf images. In: 2016 IEEE 1st international conference on power electronics, intelligent control and energy systems (ICPEICES), Delhi, pp 1–5. https://doi.org/10.1109/icpeices.2016.7853301

  87. Shrivastava S et al (2015) Color sensing and image processing-based automatic soybean plant foliar disease severity detection and estimation. Multimed Tools Appl 74:11467–11484. https://doi.org/10.1007/s11042-014-2239-0

    Article  Google Scholar 

  88. Shrivastava S et al (2017) Soybean plant foliar disease detection using image retrieval approaches. Multimed Tools Appl 76:26647–26674. https://doi.org/10.1007/s11042-016-4191-7

    Article  Google Scholar 

  89. Akram T et al (2017) Towards real-time crops surveillance for disease classification: exploiting parallelism in computer vision. Comput Electr Eng 59:15–26. https://doi.org/10.1016/j.compeleceng.2017.02.020

    Article  Google Scholar 

  90. Glezakos TJ et al (2010) Plant virus identification based on neural networks with evolutionary preprocessing. Comput Electron Agric 70:263–275. https://doi.org/10.1016/j.compag.2009.09.007

    Article  Google Scholar 

  91. Huang T et al (2018) Detecting sugarcane borer diseases using support vector machine. Inf Process Agric. https://doi.org/10.1016/j.inpa.2017.11.001

    Article  Google Scholar 

  92. TN Tete, S Kamlu (2017) Detection of plant disease using threshold, K-mean cluster and ANN algorithm. In: 2017 2nd international conference for convergence in technology (I2CT), Mumbai, pp 523–526. https://doi.org/10.1109/i2ct.2017.8226184

  93. Gaikwad VP, Musande V (2017) Wheat disease detection using image processing. In: 2017 1st international conference on intelligent systems and information management (ICISIM), Aurangabad, pp 110–112. https://doi.org/10.1109/icisim.2017.8122158

  94. Singh V, Misra AK (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf Process Agric 4:41–49. https://doi.org/10.1016/j.inpa.2016.10.005

    Article  Google Scholar 

  95. Xiong X et al (2017) Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization. Plant Methods 13:104. https://doi.org/10.1186/s13007-017-0254-7

    Article  Google Scholar 

  96. Bai X et al (2016) A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images. Comput Electron Agric 136:157–165. https://doi.org/10.1016/j.compag.2017.03.004

    Article  Google Scholar 

  97. Li Y et al (2016) In-field cotton detection via region-based semantic image segmentation. Comput Electron Agric 127:475–486. https://doi.org/10.1016/j.compag.2016.07.006

    Article  Google Scholar 

  98. Atoum Y et al (2016) On developing and enhancing plant-level disease rating systems in real fields. Pattern Recognit 53:287–299. https://doi.org/10.1016/j.patcog.2015.11.021

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Siddharth Singh Chouhan.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chouhan, S.S., Singh, U.P. & Jain, S. Applications of Computer Vision in Plant Pathology: A Survey. Arch Computat Methods Eng 27, 611–632 (2020). https://doi.org/10.1007/s11831-019-09324-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-019-09324-0

Navigation