Bioinformatics and Image Processing—Detection of Plant Diseases

  • N. Hanuman Reddy
  • E. Ravi KumarEmail author
  • M. Vinay Reddy
  • K. L. Raghavender Reddy
  • G. Susmitha Valli
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 815)


This paper gives an idea of how a combination of image processing along with bioinformatics detects deadly diseases in plants and agricultural crops. These kinds of diseases are not recognizable by bare human eyesight. First occurrence of these diseases is microscopic in nature. If plants are affected with such kind of diseases, there is deterioration in the quality of production of the plants. We need to correctly identify the symptoms, treat the diseases, and improve the production quality. Computers can help to make correct decision as well as can support industrialization of the detection work. We present in this paper a technique for image segmentation using HSI algorithm to classify various categories of diseases. This technique can also classify different types of plant diseases as well. GA has always proven itself to be very useful in image segmentation.


Image processing Bioinformatics Image segmentation Plant diseases Classification 


  1. 1.
    Monika Jhuria, Ashwani Kumar, Rushikesh Borse, Image Processing For Smart Farming: Detection of Disease and Fruit Grading, IEEE Proceedings of the 2013 IEEE Second International Conference on Image Information Processing, 2013, p. 521–526.Google Scholar
  2. 2.
    Shiv Ram Dubey, Anand Singh Jalal, Detection and Classification of Apple Fruit Diseases using Complete Local Binary Patterns IEEE, Third international conference on Computer and communication Technology, 2012, p. 247–251.Google Scholar
  3. 3.
    Ilaria Pertot, Tsvi Kuflik, Igor Gordon, Stanley Freeman, Yigal Elad, Identificator: A web-based tool for visual plant disease identification, a proof of concept with a case study on strawberry, Computers and Electronics in Agriculture, Elsevier, 2012, Vol.88, p. 144–154.Google Scholar
  4. 4.
    Xiaoou Tang, Fang Wen, IntentSearch: Capturing User Intention for One-Click Internet Image Search, IEEE transactions on pattern analysis and machine intelligence, 2012, vol.34, p. 1342–1353.CrossRefGoogle Scholar
  5. 5.
    R.Gonzalez, R. Woods, Digital Image Processing, 3rd ed., Prentice- Hall, 2007. 7. CROPSAP (Horticulture) team of ‘E’ pest surveillance: 2013: Pests of Fruits (Banana, Mango and Pomegranate) ‘E’ Pest Surveillance and Pest Management Advisory (ed. D.B. Ahuja), jointly published by National Centre for Integrated Pest Management, New Delhi and State Department of Horticulture, Commissionerate of Agriculture, Pune, MS. pp 67.Google Scholar
  6. 6.
    Parag Shinde, Amrita Manjrekar, Efficient Classification of Images using Histogram based Average Distance Computation Algorithm Extended with Duplicate Image Detection Elsevier, proc. Of Int. Conf. On advances in Computer Sciences, AETACS, 2013.Google Scholar
  7. 7.
    Patil, J. K., & Kumar, R. (2011). “Advances in image processing for detection of plant diseases”. Journal of Advanced Bioinformatics Applications and research, 2(2), 135–141.Google Scholar
  8. 8.
    Wang, H., Li, G., Ma, Z., & Li, X. (2012, May). “Image recognition of plant diseases based on principal component analysis and neural networks”. In Natural Computation (ICNC), 2012 IEEE Eighth International Conference on 29–31 May 2012, Chongqing pp. 246-251.Google Scholar
  9. 9.
    Wang,Z., Sun, X., Ma, Y., Zhang, H., Ma, Y., Xie, W., & Zhang, Y. (2014, July). “Plant recognition based on intersecting cortical model”. In Neural Networks(IJCNN), 2014 IEEE. International Joint Conference 6–11 July 2014, Beijing pp. 975–980.Google Scholar
  10. 10.
    Novak, P., & Sindelar, R. (2013, November). “Ontology-based industrial plant description supporting simulation model design and maintenance”. In Industrial Electronics Society, IECON 2013-39th Annual Conference of the IEEE 10–13 Nov. 2013, Vienna pp. 6866–6871.Google Scholar
  11. 11.
    Husin, Z. B., Shakaff, A. Y. B. M., Aziz, A. H. B. A., & Farook, R. B. S. M. (2012, February). “Feasibility study on plant chili disease detection using image processing techniques”. In Intelligent Systems, Modelling and Simulation (ISMS), 2012 IEEE Third International Conference on 8–10 Feb. 2012, Kota Kinabalu pp. 291–296.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • N. Hanuman Reddy
    • 1
  • E. Ravi Kumar
    • 2
    Email author
  • M. Vinay Reddy
    • 1
  • K. L. Raghavender Reddy
    • 1
  • G. Susmitha Valli
    • 3
  1. 1.Department of CSEVardhaman College of EngineeringHyderabadIndia
  2. 2.Department of ITVardhaman College of EngineeringHyderabadIndia
  3. 3.Department of Computer Science and EngineeringMLR Institute of TechnologyHyderabadIndia

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