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Üç Farklı Yapay Sinir Ağı Yöntemi Kullanılarak Toprak Penetrasyon Direnci Tahmini

Year 2016, Volume: 12 Issue: 2, 95 - 102, 14.10.2016

Abstract

Toprak sıkışması, toprağın fiziksel, kimyasal ve biyolojik özelliklerine olumsuz yönde etki

ederek bitki gelişimini engelleyen önemli bir problemdir. Bu problemin bitki gelişimine olan

etkilerinin belirlenebilmesi için üretim alanının birçok noktasından toprak penetrasyon direnç

verilerinin toplanması gerekmektedir. Büyük üretim alanlarından toprak penetrasyon direnç

verilerinin toplanması araştırmacılar için zaman alıcı ve yorucu bir uygulamadır. Ayrıca, ölçüm

yapılan nokta sayısı ne derecede üretim alanının tamamının değerlendirilmesinde yeterli olacağı

belirsizdir. Bu nedenle, çalışma yapılan alanın bütününü değerlendirebilmek için ölçüm yapılmayan

noktalara ait toprak penetrasyon direnç verilerinin de tahmin edilmesi gerekmektedir. Matematiksel

bir hesaplama ve modelleme yöntemi olan yapay sinir ağları, bilinen minimum test verileri ile

bilinmeyen verilerin tahmin edilmesinde kullanılan güncel bir yöntemdir. Çalışmada, 20 ha

büyüklüğündeki alanın 1603 farklı noktasından ve 40 cm derinliğinden alınan coğrafik konum ve

toprak penetrasyon direnç verileri toplanmıştır. Toplanan 1603 verinin, %24’ü test, geri kalan

%76’lık kısım eğitim için kullanılmıştır. Ölçümü yapılmayan noktalara ait direnç değerleri, Matlab

İçerisindeki Genelleştirilmiş Regresyon Sinir Ağı (Generalised Regression Neural Network, GRNN),

Çok Katmanlı Algılayıcı (MLP) ve Radyal Temelli Fonksiyonlar (RBF) yöntemleri kullanılarak tahmin

edilmiştir. Bu değerlere ek olarak mutlak hata (MSE), ortalama karekök hatası (RMSE) ve ortalama

mutlak hata (MAE) değerleri hesaplanmıştır. Sonuç olarak Radyal Temelli Fonksiyonlar yönteminin

gerçek değerlere yakınsama durumunun iyi olduğu tespit edilmiştir.

References

  • Abrougui K, Chehaibi S, Louvet JN, Hannachi C and Destain MF (2012). Soil Structure and the Effect of Tillage Systems. Bulletin UASVM Agriculture, 69: 11-16.
  • Bayat H, Neyshabouri MR, Hajabbasi MA, Mahboubi AA, Mosaddeghi MR (2008). Comparing neural networks,
  • linear and nonlinear regression techniques to model penetration resistance. Turkish Journal of Agricultural Forestry, 32: 1–9.
  • Bocco M, Obando G, Sayago S and Willington E (2007). Neural network models for land cover classification from satellite images. Agric. Téc., 67(4): 414-421.
  • Bongiovanni R and Lowenberg-Deboer J (2004). Precision Agriculture and Sustainability. Precision Agriculture, 5(4): 359-387.
  • Braga RP (2000). Predicting the spatial pattern of grain yield under water limiting conditions. University of Florida, PhD thesis, Florida.
  • Üç Farklı Yapay Sinir Ağı Yöntemi Kullanılarak Toprak Penetrasyon Direnci Tahmini 102 Cerana J, Wilson M, Pozzolo O, De Battista JJ, Rivarola S, and Díaz E (2005). Relaciones mate-máticas entre la resistencia mecánica a la pene-tración y el contenido hídrico en un Vertisol. Estudios de la Zona no Saturada del Suelo, 7: 159-163.
  • Cheng CB, Lee ES (2001) Fuzzy Regression With Radial Basis Function Network.Fuzzy Sets and Systems, 119: 291- 301.
  • Díaz CG, Osinaga R and Arzeno J (2010). Resistencia a la penetración, humedad del suelo y densidad aparente como indicadores de calidad de suelos en parcelas de largo plazo. XXII Congreso Argentino de la Ciencia del Suelo, Rosario, Argentina.
  • Ehlers W, Köpke U, Hesse F and Bohm W (1983). Penetration resistance and root growth of oats in tilled and untilled loess soil. Soil Tillage Res., 3: 261-275. Fausett LV (1994). Fundamentals neural Networks: Architecture, algorithms, and applications, Englewood Cliffs, New Jersey, 1-449.
  • García I, Rodríguez JG, López F and Tenorio YM (2010). Transporte de contaminantes en aguas subterráneas mediante redes neuronales artificiales. Inf. Tecnol, 21(5):79-86.
  • Goyal S and Goyal G K (2011). Cascade and feed-forward backpropagation artificial neural network models for prediction of sensory quality of instant coffee flavoured sterilized drink. Can. J. Artif. In-tell. Machine Learn. Pattern Recog. 2(6):78 - 82.
  • Györfi L, Kohler M, Krzyak A, Walk H (2002). Distribution Free Theory of Nonparametric Regression, Springer- Verlag, New York.
  • Hashimoto Y (1997). Application artificial neural network and genetic algorithms to agricultural systems. Computer and Electronics in Agriculture, 18: 71-72.
  • Haykin S (1999). Neural networks: a comprehensive foundation. 2nd edition. Prentice Hall, 1-842.
  • Hecht NR, (1990). Neurocomputing. Addison-Weseley, MA, 147-153.
  • Holguín NJV, Salcedo LOG, Will ALE (2011). Prediction of soils penetration strength using artificial neural networks. Acta Agronómica, 60(3): 251-260.
  • Hossam F, Mouhammd A and Ali R (2013). Artificial Neural Networks for Surface Ozone Prediction: Models and Analysis. Pol. J. Environ. Stud., 23(2): 341-348 Kandırmaz HM, Kaba K ve Avci M (2014). Estimation Of Monthly Sunshine Duration In Turkey Using Artificial Neural Networks. International Journal of Photoenergy, 2014: 1-9.
  • Klir GJ and Yuan B (1995). Fuzzy Sets and Fuzzy Logic: Theory and Application. Prentice Hall International Inc., New Jersey.
  • Lippmann RP (1987). An introduction to computing with neural nets. IEEE ASSP Mug., 4: 22.
  • Liu J, Goering CE and Tian L (2001). Neural network for setting target corn yields. Trans. ASAE, 44(3): 705-713. Maren A, Harston C and Pap R (1990). Handbook of neural computing applications, Academic Press, McClelland, 1- 483.
  • Mas JF, Puig H, Palacio JL, Sosa A (2002). Modelado del proceso de deforestación en una región del sureste de México. Memorias del II Seminario Latinoamericano de Geografía Física, pp. 24-27, Maracaibo, Venezuela.
  • Miller RE, Hazard J and Howes J (2001) Precision, Accuracy, and Efficiency of Four Tools for Measuring Soil Bulk Density or Strength. USDA Forest Service Pacific Northwest Research Station Gen. Tech Report PNW-RP- 532, April 2001.
  • Montana Moreno JJ, Palmer Pol A, Munoz Garcia P (2011). Artificial neural networks applied to forecasting time series. Psicothema, 23(2): 322-329.
  • Önalp A ve Arel E (2011). Geoteknik Mühendisliğinde Yapay Sinir Ağı Uygulamaları ve Bir Örnek: Zemin ProfilininTahmin Edilmesi. İTÜ Mühendislik Dergisi, d: 3- 14.
  • Pinto FAC, Reid JF, Zang Q and Noguchi N (1999). Guidance parameter determination using artificial neural network classifier. ASAE Paper No. 993004, St. Joseph, Michigan. Ripley BD (1996). Pattern recognition and neural networks. Cambridge University Press, Cambridge, 1-416.
  • Rubio C (2005) Hidrodinámica de los suelos de un área de montaña media mediterránea sometida a cambios de uso y cubierta. Universidad Autónoma de Barcelona, PhD thesis, Barcelona.
  • Rumelhart DE and McClelland JL (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press, Cambridge, 1-567.
  • Topakci M, Unal I, Canakci M Celik HK, Karayel D (2010). Design of a Horizontal Penetrometer for Meas uring Onthe- Go Soil Resistance. Sensors, 10: 9337-9348.
  • Vaz CMP, Luis HB and Hopmans JW (2001). Contribution of water content and bulk density to field soil penetration resistance as measured by a combined cone penetrometer-TDR probe. Soil Till. Res, 60: 35-42.
  • Zurada JM (1992). Introduction to artificial neural systems. West Publishing Company, St. Paul, 1-758.
Year 2016, Volume: 12 Issue: 2, 95 - 102, 14.10.2016

Abstract

References

  • Abrougui K, Chehaibi S, Louvet JN, Hannachi C and Destain MF (2012). Soil Structure and the Effect of Tillage Systems. Bulletin UASVM Agriculture, 69: 11-16.
  • Bayat H, Neyshabouri MR, Hajabbasi MA, Mahboubi AA, Mosaddeghi MR (2008). Comparing neural networks,
  • linear and nonlinear regression techniques to model penetration resistance. Turkish Journal of Agricultural Forestry, 32: 1–9.
  • Bocco M, Obando G, Sayago S and Willington E (2007). Neural network models for land cover classification from satellite images. Agric. Téc., 67(4): 414-421.
  • Bongiovanni R and Lowenberg-Deboer J (2004). Precision Agriculture and Sustainability. Precision Agriculture, 5(4): 359-387.
  • Braga RP (2000). Predicting the spatial pattern of grain yield under water limiting conditions. University of Florida, PhD thesis, Florida.
  • Üç Farklı Yapay Sinir Ağı Yöntemi Kullanılarak Toprak Penetrasyon Direnci Tahmini 102 Cerana J, Wilson M, Pozzolo O, De Battista JJ, Rivarola S, and Díaz E (2005). Relaciones mate-máticas entre la resistencia mecánica a la pene-tración y el contenido hídrico en un Vertisol. Estudios de la Zona no Saturada del Suelo, 7: 159-163.
  • Cheng CB, Lee ES (2001) Fuzzy Regression With Radial Basis Function Network.Fuzzy Sets and Systems, 119: 291- 301.
  • Díaz CG, Osinaga R and Arzeno J (2010). Resistencia a la penetración, humedad del suelo y densidad aparente como indicadores de calidad de suelos en parcelas de largo plazo. XXII Congreso Argentino de la Ciencia del Suelo, Rosario, Argentina.
  • Ehlers W, Köpke U, Hesse F and Bohm W (1983). Penetration resistance and root growth of oats in tilled and untilled loess soil. Soil Tillage Res., 3: 261-275. Fausett LV (1994). Fundamentals neural Networks: Architecture, algorithms, and applications, Englewood Cliffs, New Jersey, 1-449.
  • García I, Rodríguez JG, López F and Tenorio YM (2010). Transporte de contaminantes en aguas subterráneas mediante redes neuronales artificiales. Inf. Tecnol, 21(5):79-86.
  • Goyal S and Goyal G K (2011). Cascade and feed-forward backpropagation artificial neural network models for prediction of sensory quality of instant coffee flavoured sterilized drink. Can. J. Artif. In-tell. Machine Learn. Pattern Recog. 2(6):78 - 82.
  • Györfi L, Kohler M, Krzyak A, Walk H (2002). Distribution Free Theory of Nonparametric Regression, Springer- Verlag, New York.
  • Hashimoto Y (1997). Application artificial neural network and genetic algorithms to agricultural systems. Computer and Electronics in Agriculture, 18: 71-72.
  • Haykin S (1999). Neural networks: a comprehensive foundation. 2nd edition. Prentice Hall, 1-842.
  • Hecht NR, (1990). Neurocomputing. Addison-Weseley, MA, 147-153.
  • Holguín NJV, Salcedo LOG, Will ALE (2011). Prediction of soils penetration strength using artificial neural networks. Acta Agronómica, 60(3): 251-260.
  • Hossam F, Mouhammd A and Ali R (2013). Artificial Neural Networks for Surface Ozone Prediction: Models and Analysis. Pol. J. Environ. Stud., 23(2): 341-348 Kandırmaz HM, Kaba K ve Avci M (2014). Estimation Of Monthly Sunshine Duration In Turkey Using Artificial Neural Networks. International Journal of Photoenergy, 2014: 1-9.
  • Klir GJ and Yuan B (1995). Fuzzy Sets and Fuzzy Logic: Theory and Application. Prentice Hall International Inc., New Jersey.
  • Lippmann RP (1987). An introduction to computing with neural nets. IEEE ASSP Mug., 4: 22.
  • Liu J, Goering CE and Tian L (2001). Neural network for setting target corn yields. Trans. ASAE, 44(3): 705-713. Maren A, Harston C and Pap R (1990). Handbook of neural computing applications, Academic Press, McClelland, 1- 483.
  • Mas JF, Puig H, Palacio JL, Sosa A (2002). Modelado del proceso de deforestación en una región del sureste de México. Memorias del II Seminario Latinoamericano de Geografía Física, pp. 24-27, Maracaibo, Venezuela.
  • Miller RE, Hazard J and Howes J (2001) Precision, Accuracy, and Efficiency of Four Tools for Measuring Soil Bulk Density or Strength. USDA Forest Service Pacific Northwest Research Station Gen. Tech Report PNW-RP- 532, April 2001.
  • Montana Moreno JJ, Palmer Pol A, Munoz Garcia P (2011). Artificial neural networks applied to forecasting time series. Psicothema, 23(2): 322-329.
  • Önalp A ve Arel E (2011). Geoteknik Mühendisliğinde Yapay Sinir Ağı Uygulamaları ve Bir Örnek: Zemin ProfilininTahmin Edilmesi. İTÜ Mühendislik Dergisi, d: 3- 14.
  • Pinto FAC, Reid JF, Zang Q and Noguchi N (1999). Guidance parameter determination using artificial neural network classifier. ASAE Paper No. 993004, St. Joseph, Michigan. Ripley BD (1996). Pattern recognition and neural networks. Cambridge University Press, Cambridge, 1-416.
  • Rubio C (2005) Hidrodinámica de los suelos de un área de montaña media mediterránea sometida a cambios de uso y cubierta. Universidad Autónoma de Barcelona, PhD thesis, Barcelona.
  • Rumelhart DE and McClelland JL (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press, Cambridge, 1-567.
  • Topakci M, Unal I, Canakci M Celik HK, Karayel D (2010). Design of a Horizontal Penetrometer for Meas uring Onthe- Go Soil Resistance. Sensors, 10: 9337-9348.
  • Vaz CMP, Luis HB and Hopmans JW (2001). Contribution of water content and bulk density to field soil penetration resistance as measured by a combined cone penetrometer-TDR probe. Soil Till. Res, 60: 35-42.
  • Zurada JM (1992). Introduction to artificial neural systems. West Publishing Company, St. Paul, 1-758.
There are 31 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

İlker Ünal

Salih Sözer This is me

Önder Kabaş This is me

Süleyman Çetin This is me

Mehmet Topakcı This is me

Publication Date October 14, 2016
Published in Issue Year 2016 Volume: 12 Issue: 2

Cite

APA Ünal, İ., Sözer, S., Kabaş, Ö., Çetin, S., et al. (2016). Üç Farklı Yapay Sinir Ağı Yöntemi Kullanılarak Toprak Penetrasyon Direnci Tahmini. Tarım Makinaları Bilimi Dergisi, 12(2), 95-102.

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