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An Identification of Chlorophyll Content using Image Processing Technique and Fuzzy Mamdani Method

Affiliations

  • State Islamic University of Maulana Malik Ibrahim Malang, Indonesia

Abstract


Background/Objectives: To improve an identification of chlorophyll content in leaf, this paper presents an implementation of a supervised learning method based on membership function training in the context of mamdani fuzzy models. Methods/Statistical Analysis: this paper presents a fuzzy rule based algorithm; natural color image in leaf (Red, Green and Blue) is used as the input to the fuzzy mamdani models. The output of the fuzzy mamdani models is value of chlorophyll content. Results: The proposed approach was superior to identification of chlorophyll content in leaf using image processing technique and mamdani fuzzy method has higher identification accuracy. Conclusion/Application: Finally, the basic difference of value of chlorophyll content between fuzzy mamdani models and the actual was less than 3,1 % on average.

Keywords

Chlorophyll Content, Identification, Image Processing, Leaf, Mamdani Fuzzy.

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References


  • Robert JP. The chequered history of the development and use of simultaneous equations for the accurate determination of chlorophylls a and b. Journal of Photosynthesis Research. 2002; 73(6):149–56.

  • Coops NC, Stone C, Culvenor DS, Chisholm L, Merton R. Chl Content in Eucalypt Vegetation at the Leaf and Canopy Level as Derived from High Spectral Resolution Data. Journal of Tree Physiology. 2003 Jan; 23(1):23–31.

  • Haboudane D, Miller JR, Tremblay N, Zarco-Tejad PJ, Dextraze L. Integrated narrow-band vegetation indices for prediction of crop chl content for application to precision agriculture. Journal of Remote Sensing of Environment. 2002; 81(2):416–26.

  • Haboudane D, Miller J, Tremblay N, Zarco Tejad P, Dextraze L. Integrated narrow-band vegetation indices for prediction of crop chl content for application to precision agriculture. Journal of Remote Sensing of Environment. 2002 Aug; 81(2-3):416–26.

  • Hartmut KL, Fatbardha B. Detection of photosynthetic activity and water stress by imaging the red Chl fluorescence. Journal of Plant Physiology. 2000; 38(11):889–95.

  • Yoder BJ, Pettigrew Crosby RE. Predicting Nitrogen and Chl content and concentrations from reflectance spectra (400-2500 Nm) at leaf and canopy scales. Journal of Remote Sensing of Environment. 1995; 53(2):199–211.

  • Haboudane D, Miller J, Pattey E, ZarcoTejad P, Strachan I. Hyperspectral vegetation indices and novel algorithms for predicting green lai of crop canopies: Modeling and validation in the context of precision agriculture. Journal of Remote Sensing of Environment. 2004; 90(3):337–52.

  • Huang Z, Turner BJ, Dury SJ, Wallis IR, Foley WJ. Estimating foliage nitrogen concentration from hymap data using continuum removal analysis. Journal of Remote Sensing of Environment. 2004; 93(4):18–29.

  • Kim MS, Mcmurtrey JE, Mulchi CL, Daughtry CST, Chappelle EW, Chen YR. Steady-State Multispectral Fluorescence Imaging System for Plant Leaves. Journal of Application Optics. 2001; 40(2):157–66.

  • Park H-J, Seo S-T, Song B-S. Clinical decision support system for patients with cardiopulmonary function using image processing. Indian Journal of Science and Technology. 2015 Apr; 8(8):83–8.

  • Spomer LA, Smith MAL, Sawwan JS. Rapid, nondestructive measurement of Chl content in leaves with non uniform Chl distribution. Journal of Photosynthesis Research. 1988; 16(3):277–84.

  • Everitt JH, Escobar DE, Villarreal R, Noriega JR, Davis MR. Airborne video systems for agricultural assessment. Journal of Remote Sensing of Environment. 1991; 35(2):231–42.

  • Anatoly AG, Yoram JK, Robert S, Don R. Novel algorithms for remote estimation of vegetation fraction. Journal of Remote Sensing of Environment. 2002; 80(1):76–87.

  • Benedict HM, Swidler R. Non destructive method for estimating chlorophyll content of leaves. Science. 1961; 133(3469):2015–2016.

  • Inada K. Studies on a method for determining the deepness of green color and chlorophyll content of intact crop leaves and its practical application. 1. Principal for estimating the deepness of green color and chlorophyll content of whole leaves. Proceedings of Crop Science Society Japan. 1964; 32(2):157–62.

  • Takano Y, Tsunoda S. Light reflection, transmission, and absorption rates of rice leaves in relation to their chlorophyll and nitrogen content. Tohoku Journal of Agricultural Research. 1970; 21(3-4):111–7.

  • Wallihan EF. Meter for estimating chlorophyll concentrations in leaves Portable reflectance. Agronomy Journal. 1973; 65(4):659–62.

  • Hardwick K, Baker NR. In.i.o measurement of chlorophyll content of leaves. New Phytologist. 1973; 72(1):51–4.

  • Macnicol PK, Dudzinski ML, Condon BN. Estimation of chlorophyll in tobacco leaves by direct photometry. Annals of Botany. 1976; 40(1):143–52.

  • Gao J. Canopy Chl Estimation with Hyperspectral Remote Sensing. PhD Thesis, University of Kansas, Manhattan.

  • Kawashima S, Nakatani M. An algorithm for estimating Chl content in leaves using a video camera. Journal of Annals of Botany. 1998; 81(1):49–54.

  • Mahmoodi M, Khazaei J, Vahdati K. Identification of Walnut Genotype Using Image Processing Techniques. In Agricultural Engineering of the Crete Conference. 2008; 1(1). p. 1610–5.

  • Ohta Y. Knowledge-Based Interpretation of Outdoor Natural Colour Scenes. Pitman Publishing Inc, 1985.

  • Spomer LA, Smith MAL, Sawwan JS. Rapid, nondestructive measurement of chlorophyll content in leaves with non uniform chlorophyll distribution. Photosynthesis Research. 1988; 16:277–84.

  • Everitt JH, Escobar DE, Villarreal R, Noriega JR, Davis MR. Airborne video systems for agricultural assessment. Remote Sensing of Environment.1991; 35(2-3):231–42.

  • Takagi T, Sugeno M. Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Trans System, Man, Cyber. 1985; 15(1):116–32.

  • Suhartono MH, Purnomo MH. Integration of Fuzzy System into Genetic L-System Programming based plant modeling environment with mathematica. Australian Journal of Basic and Applied. 2011; 5(11):1760–5.

  • Suhartono MH, Purnomo MH. Plant Growth Modeling of Zinnia Elegans Jacq using Fuzzy Mamdani and L System Approach with Mathematica. Journal of Theoretical and Applied Information Technology. 2013; 50(1):1–6.


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