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Görüntü İşleme Tekniklerinin Sürdürülebilir Tarımdaki Yeri ve Önemi: Literatür Çalışması

Year 2016, Volume: 12 Issue: 3, 199 - 206, 14.10.2016

Abstract

Bu çalışmada, görüntü işleme tekniklerinin sürdürülebilir tarım stratejileri arasındaki yeri ve

öneminin değerlendirilmesi yapılmıştır. Sürdürülebilir tarıma yönelik 2011-2016 yılları arasında

görüntü işleme teknikleri ile ilgili çalışmalara ait yayınlar üzerinde sentez yapılarak bu konuda ileriye

yönelik araştırma gereksinimi olan alanların ortaya çıkarılması hedeflenmiştir.

Görüntü işleme tekniklerinin, özellikle sulama, gübreleme, ilaçlama başta olmak üzere tarımsal

girdilerde etkinliğin arttırılmasına olanak sağlaması ve diğer alanlardaki başarılı uygulamalarıyla

sürdürülebilir tarıma önemli ölçüde katkı sağladığı görülmüştür. Yöntemin, anlık olarak vejetasyon

tayini, toprak nem içeriğinin belirlenmesi, hastalık ve yabancı ot lokasyonlarının saptanması,

tarımsal materyallerin sınıflandırılması gibi uygulamalarda sayısallaştırmaya elverişli olması ve

otomasyona aktarılabilme yeteneği bu katkının sağlanmasında önemli yer tutmaktadır. Ancak

yapılan çalışmaların pratiğe aktarılması hususunda eksiklikler gözlenmekte ve bu konuda

organizasyonun sağlanması ve desteklerin arttırılması gerekmektedir. Böylece görüntü işleme

tekniklerinin sürdürülebilir tarıma katkısının da giderek artacağı tahmin edilmektedir.

References

  • Anonim (2016). http:// tr.wikipedia.org (Accessed to web:20.05.2016).
  • Artizzu X P B, Ribeiro A, Guijarro M, Pajares G (2011). Realtime image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture, 75: 337–346.
  • Barbedo J G A (2014). Using digital image processing for counting whiteflies on soybean leaves. Journal of Asia- Pacific Entomology, 17: 685–694.
  • Cai X, Sun Y, Zhao Y, Damerow L, Lammers P S, Sun W, Lin J, Zheng L ve Tang Y (2013). Smart detection of leaf wilting by 3D image processing and 2D Fourier transform.
  • Computers and Electronics in Agriculture, 90: 68-75.
  • Dutta M K, Issac A, Minhas N, Sarkar B (2016). Image processing based method to assess fish quality and freshness. Journal of Food Engineering, 177: 50-58.
  • Ellahyani A, El Ansari M, El Jaffari I (2016). Trafic sign detection and recognition based on random forests. Applied Soft Computing, In Press:Corrected proof.
  • FAO (2013). Food and Agriculture Organization of the United Nations, FAO Statistics Division, http://www.faostat.org (Erişim tarihi: Mayıs 2015).
  • Fuentes S, Palmer A R, Taylor D, Zeppel M, Whitley R, Eamus D (2008). Anautomated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application toan examination of the relationship between remotely sensed and field measurements of LAI. Funct. Plant Biol. 35: 1070–1079.
  • Guijarro M, Pajares G, Riomoros I, Herrera P J, Artizzu X P B, Ribeiro A (2011). Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture, 75: 75–83.
  • Harrison S, Carrol B, Beasley J, Baraniuk R (2004). Optimization of image recognition: Fingerprint Matching. http://cnx.org (Accesed to web: 15.05.2016).Jiang G, Wang Z, Liu H (2015). Automatic detection of crop rows based on multi-ROIs. Expert Systems with Applications, 42: 2429-2441.
  • Keresztes J C, Goodarzi M, Saeys W (2016). Real-time pixel based early apple bruise detection using short wave infrared hyperspectral imaging in combination with calibration and glare correction techniques. Food Control, 66: 215-226.
  • Linker R, Cohen O, Naor A (2012). Determination of the number of green apples in RGB images recorded in orchards. Computers and Electronics in Agriculture, 81: 45–57.
  • Mateos G G, Hernández J L H, Henarejos D E, Terrones S J, Martínez J M M (2015). Study and comparison of color models for automatic image analysis inirrigation management applications. Agricultural Water Management, 151: 158-166.
  • Montalvo M, Pajares G, Guerrero J M, Romeo J, Guijarro M, Ribeiro A, Ruz J J, Cruz J M (2012). Automatic detection of crop rows in maize fields with high weeds pressure. Expert Systems with Applications, 39: 11889-11897.
  • Mora M, Avila F, Benavides M C, Maldonado G, Cáceres J O, Fuentes S (2016). Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photograpies. Computers and Electronics in Agriculture, 123:195-202.
  • Pacheco D G F, Henarejos D E, Canales A R, Conesa J, Martinez J M M (2014). A digital image-processing-based method for determining the crop coefficient of lettuce crops in the southeast of Spain. Biosystems Engineering, 117: 23-34.
  • Pourreza A, Pourreza H, Fard M H A, Sadrnia H (2012). Identification of nine Iranian wheat seed varieties by textural analysis with image processing. Computers and Electronics in Agriculture, 83: 102–108.
  • Pujari J D, Yakkundimath R, Byadgı A (2015). Image processing based detection of fungal diseases in plants. Procedia Computer Science, 46:1802-1808.
  • Rahman M, Blackwell B, Banerjee N, Saraswat D (2015). Smartphone-based hierarchical crowdsourcing for weed identification. Computers and Electronics in Agriculture, 113: 14–23.
  • Reis M J C S, Morais R, Peres E, Pereira C, Contente O, Soares S, Valente A, Baptista J, Ferreira P J S G, Cruz J B (2012). Automatic detection of bunches of grapes in natural environment from color images. Journal of Applied Logic ,10: 285–290.
  • Sanchez L O S, Miranda R C, Escalante J J G, Pacheco I T, González R G G, Miranda C L C,
  • Lumbreras P D A (2011). Scale invariant feature approach for insect monitoring. Computers and Electronics in Agriculture, 75: 92–99.
  • Sanchis J G, Guerrero J D M, Olivas E S, Sober M M, Benedito R M, Blasco J (2012). Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniques. Expert Systems with Applications, 39: 780–785.
  • Solis-Sánchez L O, García-Escalante J J, Casta˜neda-Miranda R, Torres-Pacheco I, Guevara González R G, (2009). Machine vision algorithm for whiteflies (Bemisia tabaci Genn.) scouting under greenhouse environment. Journal of Applied Entomology, 133 (7):546–552.
  • Tang J, Chen X, Miao R, Wang D (2016). Weed detection using image processing under different illumination for site-specific areas spraying. Computers and Electronics in Agriculture, 122: 103–111.
  • Tellaeche A, Pajares G, Artizzu X P B, Ribeiro A (2011). A computer vision approach for weeds identification through Support Vector Machines. Applied Soft Computing, 11: 908–915.
  • Wang D C, Yang Y, Qiang Z J, Kai Z H, Fei L (2014). Detection of thrips defect on Green-Peel Citrus using hyperspectral imaging technology combining PCA and Bspline lighting correction method. Journal of Integrative Agriculture, 13(10): 2229-2235.
  • Yao Q, Xıan D, Lıu Q1, Yang B, Dıao G, Tang J (2014). Automated counting of rice planthoppers in paddy fields based on image processing. Journal of Integrative Agriculture, 13(8): 1736-1745.
Year 2016, Volume: 12 Issue: 3, 199 - 206, 14.10.2016

Abstract

References

  • Anonim (2016). http:// tr.wikipedia.org (Accessed to web:20.05.2016).
  • Artizzu X P B, Ribeiro A, Guijarro M, Pajares G (2011). Realtime image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture, 75: 337–346.
  • Barbedo J G A (2014). Using digital image processing for counting whiteflies on soybean leaves. Journal of Asia- Pacific Entomology, 17: 685–694.
  • Cai X, Sun Y, Zhao Y, Damerow L, Lammers P S, Sun W, Lin J, Zheng L ve Tang Y (2013). Smart detection of leaf wilting by 3D image processing and 2D Fourier transform.
  • Computers and Electronics in Agriculture, 90: 68-75.
  • Dutta M K, Issac A, Minhas N, Sarkar B (2016). Image processing based method to assess fish quality and freshness. Journal of Food Engineering, 177: 50-58.
  • Ellahyani A, El Ansari M, El Jaffari I (2016). Trafic sign detection and recognition based on random forests. Applied Soft Computing, In Press:Corrected proof.
  • FAO (2013). Food and Agriculture Organization of the United Nations, FAO Statistics Division, http://www.faostat.org (Erişim tarihi: Mayıs 2015).
  • Fuentes S, Palmer A R, Taylor D, Zeppel M, Whitley R, Eamus D (2008). Anautomated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application toan examination of the relationship between remotely sensed and field measurements of LAI. Funct. Plant Biol. 35: 1070–1079.
  • Guijarro M, Pajares G, Riomoros I, Herrera P J, Artizzu X P B, Ribeiro A (2011). Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture, 75: 75–83.
  • Harrison S, Carrol B, Beasley J, Baraniuk R (2004). Optimization of image recognition: Fingerprint Matching. http://cnx.org (Accesed to web: 15.05.2016).Jiang G, Wang Z, Liu H (2015). Automatic detection of crop rows based on multi-ROIs. Expert Systems with Applications, 42: 2429-2441.
  • Keresztes J C, Goodarzi M, Saeys W (2016). Real-time pixel based early apple bruise detection using short wave infrared hyperspectral imaging in combination with calibration and glare correction techniques. Food Control, 66: 215-226.
  • Linker R, Cohen O, Naor A (2012). Determination of the number of green apples in RGB images recorded in orchards. Computers and Electronics in Agriculture, 81: 45–57.
  • Mateos G G, Hernández J L H, Henarejos D E, Terrones S J, Martínez J M M (2015). Study and comparison of color models for automatic image analysis inirrigation management applications. Agricultural Water Management, 151: 158-166.
  • Montalvo M, Pajares G, Guerrero J M, Romeo J, Guijarro M, Ribeiro A, Ruz J J, Cruz J M (2012). Automatic detection of crop rows in maize fields with high weeds pressure. Expert Systems with Applications, 39: 11889-11897.
  • Mora M, Avila F, Benavides M C, Maldonado G, Cáceres J O, Fuentes S (2016). Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photograpies. Computers and Electronics in Agriculture, 123:195-202.
  • Pacheco D G F, Henarejos D E, Canales A R, Conesa J, Martinez J M M (2014). A digital image-processing-based method for determining the crop coefficient of lettuce crops in the southeast of Spain. Biosystems Engineering, 117: 23-34.
  • Pourreza A, Pourreza H, Fard M H A, Sadrnia H (2012). Identification of nine Iranian wheat seed varieties by textural analysis with image processing. Computers and Electronics in Agriculture, 83: 102–108.
  • Pujari J D, Yakkundimath R, Byadgı A (2015). Image processing based detection of fungal diseases in plants. Procedia Computer Science, 46:1802-1808.
  • Rahman M, Blackwell B, Banerjee N, Saraswat D (2015). Smartphone-based hierarchical crowdsourcing for weed identification. Computers and Electronics in Agriculture, 113: 14–23.
  • Reis M J C S, Morais R, Peres E, Pereira C, Contente O, Soares S, Valente A, Baptista J, Ferreira P J S G, Cruz J B (2012). Automatic detection of bunches of grapes in natural environment from color images. Journal of Applied Logic ,10: 285–290.
  • Sanchez L O S, Miranda R C, Escalante J J G, Pacheco I T, González R G G, Miranda C L C,
  • Lumbreras P D A (2011). Scale invariant feature approach for insect monitoring. Computers and Electronics in Agriculture, 75: 92–99.
  • Sanchis J G, Guerrero J D M, Olivas E S, Sober M M, Benedito R M, Blasco J (2012). Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniques. Expert Systems with Applications, 39: 780–785.
  • Solis-Sánchez L O, García-Escalante J J, Casta˜neda-Miranda R, Torres-Pacheco I, Guevara González R G, (2009). Machine vision algorithm for whiteflies (Bemisia tabaci Genn.) scouting under greenhouse environment. Journal of Applied Entomology, 133 (7):546–552.
  • Tang J, Chen X, Miao R, Wang D (2016). Weed detection using image processing under different illumination for site-specific areas spraying. Computers and Electronics in Agriculture, 122: 103–111.
  • Tellaeche A, Pajares G, Artizzu X P B, Ribeiro A (2011). A computer vision approach for weeds identification through Support Vector Machines. Applied Soft Computing, 11: 908–915.
  • Wang D C, Yang Y, Qiang Z J, Kai Z H, Fei L (2014). Detection of thrips defect on Green-Peel Citrus using hyperspectral imaging technology combining PCA and Bspline lighting correction method. Journal of Integrative Agriculture, 13(10): 2229-2235.
  • Yao Q, Xıan D, Lıu Q1, Yang B, Dıao G, Tang J (2014). Automated counting of rice planthoppers in paddy fields based on image processing. Journal of Integrative Agriculture, 13(8): 1736-1745.
There are 29 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Onur Ağın

M. Zahid Malaslı This is me

Publication Date October 14, 2016
Published in Issue Year 2016 Volume: 12 Issue: 3

Cite

APA Ağın, O., & Malaslı, M. Z. (2016). Görüntü İşleme Tekniklerinin Sürdürülebilir Tarımdaki Yeri ve Önemi: Literatür Çalışması. Tarım Makinaları Bilimi Dergisi, 12(3), 199-206.

Journal of Agricultural Machinery Science is a refereed scientific journal published by the Agricultural Machinery Association as 3 issues a year.