Comparison of various filters for noise removal in paddy leaf images

  • Authors

    • K S. Archana
    • Arun Sahayadhas
    2018-04-20
    https://doi.org/10.14419/ijet.v7i2.21.12444
  • Rice plant, plant pathogen, brown Spot, image processing, preprocessing techniques.

  • Abstract

    Automatic detection of plant diseases is one the important challenging problems in agriculture field. So the basic analyzing method for automatic identification is filtering technique of preprocessing method. Hence, this Image filtering plays a important role to remove noise from image. Consequently this preprocessing method is the initial stage to make better image quality. The purpose of this paper is comparing four types of filtering techniques to differentiate the image quality in Gaussian filter, median filter, mean filter and weiner respectively filter using common data set. The image quality of overall results shows that the comparison of various filtering technique performed to enhancement quality using hybrid technique. So this paper gives best starting for researchers to automatic detection of rice plant disease detection.

     

     

  • References

    1. [1] Zhang N, Wang M & Wang N, “Precision agriculture-a worldwide overviewâ€, Comput. Electron. Agric., Vol.36, No.2–3, (2002), pp.113–132.

      [2] Wijekoon CP, Goodwin PH & Hsiang T, “Quantifying fungal infection of plant leaves by digital image analysis using Scion Image softwareâ€, J. Microbiol. Methods, Vol.74, No.2–3, (2008), pp.94–101.

      [3] Humplík JF, Lazár D, HusiÄková A & Spíchal L, “Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses–a reviewâ€, Plant Methods, (2015).

      [4] Orillo JW, Dela Cruz J, Agapito L, Satimbre PJ & Valenzuela I, “Identification of diseases in rice plant (oryza sativa) using back propagation Artificial Neural Networkâ€, International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, (2014).

      [5] Phadikar S & Sil J, “Rice Disease Identification using Pattern Recognition Techniquesâ€, No. ICCIT, (2008), pp.25–27.

      [6] AtaÅŸ M, Yardimci Y & Temizel A, “A new approach to aflatoxin detection in chili pepper by machine visionâ€, Comput. Electron. Agric., (2012).

      [7] Kanagalakshmi K & Chandra E, “Performance evaluation of filters in noise removal of fingerprint imageâ€, 3rd Int. Conf. Electron. Comput. Technol., Vol.1, (2011), pp.117–121.

      [8] Horé A & Ziou D, “Image quality metrics: PSNR vs. SSIMâ€, Proc. - Int. Conf. Pattern Recognit., (2010), pp.2366–2369.

      [9] Reza ZN, Nuzhat F, Mahsa NA & Ali H, “Detecting Jute Plant Disease Using Image Processing and Machine Learningâ€, 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), (2016), pp. 1-6.

      [10] Russo F, “A method for estimation and filtering of Gaussian noise in imagesâ€, IEEE Trans. Instrum. Meas., Vol.52, No.4, (2003), pp.1148–1154.

      [11] Agarwal SK & Kumar P, “Denoising of a Mixed Noise Color Image Using New Filter Techniqueâ€, Proc.-Int. Conf. Comput. Intell. Commun. Networks, (2015), pp.324–328.

      [12] Uddin Khan N, Arya KV & Pattanaik M, “An efficient image noise removal and enhancement methodâ€, IEEE Int. Conf. Syst. Man Cybern., (2010), pp.3735–3740.

      [13] Hoshyar AN, Al-Jumaily A & Hoshyar AN, “The beneficial techniques in preprocessing step of skin cancer detection system comparingâ€, Procedia Comput. Sci., Vol.42, (2014), pp.25–31.

      [14] Seetha MJ & MSJ, “Denoising of MRI Images using Filtering Methodsâ€, IEEE WiSPNET, (2016), pp.765–769.

      [15] Narasimha C & Rao AN, “A comparative study: Spatial domain filter for medical image enhancementâ€, International Conference on Signal Processing and Communication Engineering Systems (SPACES), (2015), pp.291-295.

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  • How to Cite

    S. Archana, K., & Sahayadhas, A. (2018). Comparison of various filters for noise removal in paddy leaf images. International Journal of Engineering & Technology, 7(2.21), 372-374. https://doi.org/10.14419/ijet.v7i2.21.12444

    Received date: 2018-05-04

    Accepted date: 2018-05-04

    Published date: 2018-04-20