Advertisement

Techniques for Plant Disease Diagnosis Evaluated on a Windows Phone Platform

  • Nikos PetrellisEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 953)

Abstract

The recognition of plant diseases is a responsibility of professional agriculture engineers. Intelligent systems can assist plant disease diagnosis in the early stages with low cost. User descriptions and image comparison are exploited in some expert systems that are already available. More sophisticated techniques like the one presented in this paper are based on features extracted from the symptoms (e.g., lesions) of a plant disease that appear on the leaves, the fruits, etc. The color, the dimensions and the number of these lesion spots can be used in some cases to discriminate the disease that has mortified a plant. In this paper, we describe a smart phone application that measures the features of the plant lesions with higher than 90% precision. The accuracy in the recognition of grapevine or citrus diseases that have been used as case studies is higher than 70% in most of the cases using only 5 photographs for the definition of each disease. The most important advantage of the proposed method is that the set of the supported diseases can be easily extended by the end-user.

Keywords

Plant disease Lesions Image processing Agricultural production 

Notes

Acknowledgement

This work is protected by the provisional patents 1009346/13-8-2018 and 1008484/12-5-2015 (Greek Patent Office).

References

  1. 1.
    Riley, M.B., Williamson, M.R., Maloy, O.: Plant disease diagnosis. Plant Health Instructor (2002).  https://doi.org/10.1094/PHI-I-2002-1021-01
  2. 2.
    Sankaran, S., Mishra, A., Eshani, R., Davis, C.: A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 72(1), 1–3 (2010)CrossRefGoogle Scholar
  3. 3.
    Patil, J., Kumar, R.: Advances in image processing for detection of plant diseases. J. Adv. Bioinform. Appl. Res. 2(2), 135–141 (2011)Google Scholar
  4. 4.
    Kulkarni, A., Patil, A.: Applying image processing technique to detect plant diseases. Int. J. Mod. Eng. Res. 2(5), 3361–3364 (2012)Google Scholar
  5. 5.
    Purcell, D.E., O’Shea, M.G., Johnson, R.A., Kokot, S.: Near infrared spectroscopy for the prediction of disease rating for Fiji leaf gall in sugarcane clones. Appl. Spectrosc. 63(4), 450–457 (2009)CrossRefGoogle Scholar
  6. 6.
    Calderon, R., Montes-Borrego, M., Landa, B.B., Navas-Cortes, J., Zarco-Tejada, P.J.: Detection of Downy Mildew of opium poppy using high-resolution multi-spectral and thermal imagery acquired with an unmanned aerial vehicle. Precision Agric. 15(6), 639–661 (2014)CrossRefGoogle Scholar
  7. 7.
    Cubero, S., et al.: Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform. Precision Agric. 15, 80–94 (2014)CrossRefGoogle Scholar
  8. 8.
    Abu-Naser, S.S., Kashkash, K.A., Fayad, M.: Developing an expert system for plant disease diagnosis. J. Artif. Intell. 1(2), 78–85 (2008)CrossRefGoogle Scholar
  9. 9.
    Deng, X.-L., Li, Z., Hong, T.S.: Citrus disease recognition based on weighted scalable vocabulary tree. Precision Agric. 15, 321–330 (2014)CrossRefGoogle Scholar
  10. 10.
    Lai, J.C., Ming, B., Li, S.K., Wang, K.R., Xie, R.Z., Gao, S.J.: An image-based diagnostic expert system for corn diseases. Agric. Sci. China 9(8), 1221–1229 (2010)CrossRefGoogle Scholar
  11. 11.
    Mix, C., Picó, F.X., Ouborg, N.J.: A comparison of stereomicroscope and image analysis for quantifying fruit traits. SEED Technol. 25(1), 12–19 (2003)Google Scholar
  12. 12.
    Chaivivatrakul, S., Dailey, M.: Texture-based fruit detection. Precision Agric. 15(6), 662–683 (2014)CrossRefGoogle Scholar
  13. 13.
    Schaad, N.W., Frederick, R.D.: Real time PCR and its application for rapid plant disease diagnostics. Can. J. Plant Pathol. 24(3), 250–258 (2002)CrossRefGoogle Scholar
  14. 14.
    Georgakopoulou, K., Spathis, C., Petrellis, N., Birbas, A.: A capacitive to digital converter with automatic range adaptation. IEEE Trans. Instrum. Meas. 65(2), 336–345 (2016)CrossRefGoogle Scholar
  15. 15.
    Petrellis, N.: Plant disease diagnosis based on image processing, appropriate for mobile phone implementation. In: 7th HAICTA 2015 Conference Proceedings, Kavala, Greece, pp. 238–246, 17–20 September 2015Google Scholar
  16. 16.
    Dark Sky weather information. https://darksky.net/forecast/40.7127,-74.0059/us12/en. Accessed 15 Mar 2018
  17. 17.
    Open Weather Map weather information. https://openweathermap.org/api. Accessed 15 Mar 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentTEI of ThessalyLarissaGreece

Personalised recommendations