Smart Media Journal (스마트미디어저널)

THE KOREAN INSTITUTE OF SMART MEDIA (한국스마트미디어학회)
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An Optimal Model Prediction for Fruits Diseases with Weather Conditions

  • Ragu, Vasanth (Sunchon National University) ;
  • Lee, Myeongbae ;
  • Sivamani, Saraswathi (Tata Consultancy Services) ;
  • Cho, Yongyun (Department of Information & communication engineering in Sunchon National University) ;
  • Park, Jangwoo (Department of Information & communication engineering in Sunchon National University) ;
  • Cho, Kyungryong (Department of Information & communication engineering in Sunchon National University) ;
  • Cho, Sungeon (Department of Information & communication engineering in Sunchon National University) ;
  • Hong, Kijeong (Department of Plant Medicine, Sunchon National University) ;
  • Oh, Soo Lyul (Department of Computer Engineering, Mokpo National University) ;
  • Shin, Changsun (Department of Information & communication engineering in Sunchon National University)
  • Received : 2018.11.23
  • Accepted : 2019.02.01
  • Published : 2019.03.31
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Abstract

This study provides the analysis and prediction of fruits diseases related to weather conditions (temperature, wind speed, solar power, rainfall and humidity) using Linear Model and Poisson Regression. The main goal of the research is to control the method of fruits diseases and also to prevent diseases using less agricultural pesticides. So, it is needed to predict the fruits diseases with weather data. Initially, fruit data is used to detect the fruit diseases. If diseases are found, we move to the next process and verify the condition of the fruits including their size. We identify the growth of fruit and evidence of diseases with Linear Model. Then, Poisson Regression used in this study to fit the model of fruits diseases with weather conditions as an input provides the predicted diseases as an output. Finally, the residuals plot, Q-Q plot and other plots help to validate the fitness of Linear Model and provide correlation between the actual and the predicted diseases as a result of the conducted experiment in this study.

Keywords

References

  1. Sindhuja Sankaran, Ashish Mishra, Reza Ehsani, and Cristina Davis, "A review of advanced techniques for detecting plant diseases", Computers and Electronics in Agriculture, vol. 72, no. 1, pp. 1-13, June 2010. https://doi.org/10.1016/j.compag.2010.02.007
  2. R. D. Berger, "Comparison of the Gompertz and Logistic Equations to Describe Plant Disease Progress", Ecology and Epidemiology, vol. 71, no. 7, pp. 716-719, 1981.
  3. L. V. Madden, and G. Hughes, "Plant Disease Incidence: Distributions, Heterogeneity, and Temporal Analysis", Annual Review of Phytopathology, vol. 33, no. 1, pp. 529-564, February 1995. https://doi.org/10.1146/annurev.py.33.090195.002525
  4. D.J. Butt, and D. J. Royle, "Multiple Regression Analysis in the Epidemiology of Plant Diseases", Springer, Berlin, Heidelberg, pp. 78-114, 1990.
  5. Rong Zhou, Shun'ichi kaneko, Fumio Tanaka, Miyuki Kayamori, Motoshige Shimizu, "Disease detection of Cercospora Leaf Spot in sugar beet by robust template matching", Computers and Electronics in Agriculture, vol. 108, pp. 58-70, October 2014. https://doi.org/10.1016/j.compag.2014.07.004
  6. Roberto oberti, Massimo Marchi, Paolo Tirelli, Aldo Calcante, Marcello Iriti, Alberto N. Borghese, "Automatic detection of powdery mildew on grapevine leaves by image analysis: Optimal view-angle range to increase the sensitivity", Computers and Electronics in Agriculture, vol. 104, pp. 1-8, June 2014. https://doi.org/10.1016/j.compag.2014.03.001
  7. A. K. Mahlein, T. Rumpf, P. Welke, H. W. Dehne, L. Plumer, U. Steiner, E. C. Oerke, "Development of spectral indices for detecting and identifying plant diseases", Remote Sensing of Environment, vol. 128, pp. 21-30, January 2013. https://doi.org/10.1016/j.rse.2012.09.019
  8. H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik and Z. ALRahamneh, "Fast and Accurate Detection and Classification of Plant Diseases", International Journal of Computer Applications, vol. 17, no. 1, pp. 31-38, March 2011. https://doi.org/10.5120/2183-2754
  9. T. Rumpf, A. K. Mahlein, U. Steiner, E. C. Oerke, H. W. Dehne, L. Plumer, "Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance", Computers and Electronics in Agriculture, vol. 74, no. 1, pp. 91-99, October 2010. https://doi.org/10.1016/j.compag.2010.06.009
  10. Vijai Singh and A.K. Misra, "Detection of plant leaf diseases using image segmentation and soft computing techniques", Information Processing in Agriculture, vol. 4, no. 1, pp. 41-49, March 2017. https://doi.org/10.1016/j.inpa.2016.10.005
  11. Dae Sun Moon, In Ho Ra, Sung Ho Kim, "Development of artificial neural network based modeling scheme for wind turbine fault detection system", Smart Media Journal, vol. 1, no. 2, pp. 47-53, 2012.
  12. Mrunalini R. Badnakhe and Prashant R. Deshmukh, "An Application of K-Means Clustering and Artificial Intelligence in Pattern Recognition for Crop Diseases", 2011 International Conference on Advancements in Information Technology, Singapore, vol. 20, pp. 134-138, 2011.
  13. Kouider Elouahed Moustafa, and Haffaf Hafid, "Self-Identification of Boundary's Nodes in Wireless Sensor Networks", Journal of Information Processing Systems, vol. 13, no. 1, pp.128-140, February 2017. https://doi.org/10.3745/JIPS.03.0062
  14. V. Bhanumathi and C. P. Sangeetha, "A guide for the selection of routing protocols in WBAN for healthcare applications", Human-centric Computing and Information Sciences, vol. 7, no. 24, pp.1-19, December 2017. https://doi.org/10.1186/s13673-016-0083-0
  15. Somnath Maity, and Jong Hyuk Park, "Powering IoT Devices: A Novel Design and Analysis Technique", Journal of Convergence, vol. 7, no. 2, pp. 1-18, October 2016.
  16. Enkhbaatar Tumernjargal, Luubaatar Badarch, Kangsan Lee, Woonchul Ham, Enkhzul Doopalam, Amartuvshin Togooch, "Implementation of Wireless PGN Analyzer for ISOBUS network", Smart Media Journal, vol. 4, no.2, pp.46-54, 2015.
  17. Jun Hyoung Kim, Jung Hyeon Park, Sung Keun Lee, Jin Gwang Koh, "Distance-based Routing Mechanism in Mobile Sensor Networks", Smart Media Journal, vol.5, no.1, pp. 55-60, 2016.
  18. Linear Regression from Wikipedia (2019), https://en.wikipedia.org/wiki/Linear_regression (02/20/2019).
  19. The Poisson Regression from Wikipedia (2019), https://en.wikipedia.org/wiki/Poisson_regression (02/20/2019).
  20. Bacterial Canker from Royal Horticultural Society (2019), https://www.rhs.org.uk/advice/profile?PID=86 (02/20/2019).
  21. Antracnose from Pestizid Aktions Netzwerk Germany (2019), http://www.oisat.org/pests/diseases/fungal/anthracnose.html (02/20/2019).
  22. Powdery Mildews from Royal Horticultural Society (2019), https://www.rhs.org.uk/advice/profile?pid=253 (02/20/2019).
  23. Bacterial Leaf Spot form Pestized Aktions Netzwerk (2019), http://www.oisat.org/pests/diseases/bacterial/bacterial_leaf_spot.html (02/20/2019).
  24. Sclerotinia Sclerotiorum from EPPO Global Database (2019), https://gd.eppo.int/taxon/SCLESC(02/20/2019).
  25. Sclerotinia Sclerotiorum from Wikipedia (2019), https://en.wikipedia.org/wiki/Sclerotinia_sclerotiorum (02/20/2019).