Using of Artificial Neural Networks in agricultural research

Yıl 2011, Cilt: 48 Sayı: 1, 71 - 76, 01.03.2011

Yakut Gevrekçi E.dilşat Yeğenoğlu Yavuz Akbaş , Meltem Sesli

Öz

Anahtar Kelimeler

-

Yapay Sinir Ağlarının Tarımsal Alanda Kullanımı

Öz

1 Ege Üniversitesi Ziraat Fakültesi, Zootekni Bölümü, Biyometri-Genetik ABD, 35100, Bornova-İzmir. * e-posta: [e-mail] 2 Celal Bayar Üniversitesi, Akhisar Meslek Yüksekokulu, 45210, Akhisar-Manisa. 3 Celal Bayar Universitesi, Tütün Eksperliği Yüksekokulu

Anahtar Kelimeler

-

Kaynakça

• Ajakaiye, A. S., A. B. Adeyemo, Osofisan A.O, and A.P.A Olowu 2006. Analysis of poultry birds production performance using artificial neural networks. Asian Journal of Information Technology., 5(5) : 522-527.
• Ahmadi, H., M. Mottaghitalab, and N., Nariman-Zadeh 2007. Group Method of Data Handling-Type Neural Network Prediction of Broiler Performance Based on Dietary Metabolizable Energy, Methionine, and Lysine. J. App.Poult. Res.,
• Arbib, M 2003. The handbook of brain theory and neural networks, MIT Press, Cambridge, MA.
• Barranco, D., A. Cimato, P. Fiorino, L. Rallo, A. Touzani, C. Castañeda, F. Serafín and I. Trujillo 2000. World Catalogue of olive varieties, 360 pp. Consejo Oleícola Internacional, Madrid, España.
• Chtioui, Y., D. Bertrand, M. F. Devaux, D. Barba 1997. Comparison of Multilayer perceptron and probabilistic neural networks in artificial vision: Application to the Discrimination of Seeds. J Chemom., 11:111-29.
• Civalek, Ö 1998. Nöro-Fuzzy Tekniği ile Dikdörtgen Plakların Analizi. III. Ulusal Hesaplamalı Mekanik Konferansı, 16-18 Kasım, İstanbul, s: 518-524.
• Çoban, H 2004. Application of an artificial neural network (ANN) for the identification of grapevine (Vitis vinifera L.) genotypes. Asian Journal of plant Sciences. 3 (30):340-343.
• Demuth, H., Beale, M., Hagan, M 2006. “Neural Network Toolbax, for use with MATLAB”, “User Guide Version 5”.
• Fernández C. , E. Soria, P. Sánchez-Seiquer, L. Gómez-Chova, R. Magdalena, A. J. Serrano 2007. Weekly milk prediction on dairy goats using neural networks. Neural Computing & Applications. 16: 373-381.
• Ganino, T., D. Beghé, S. Valenti, R. Nisi and A. Fabbri 2007. A RAPD and SSR markers for characterization and identification of ancient cultivars of Olea europaea L. in the Emilia region, Northern Italy. Genet. Resour. Crop Evol., 54., pp. 1531–1540.
• Gemas V.J.V., M.C. Almadanim, R. Tenreiro, A. Martins and P. Fevereiro 2004. Genetic diversity in the Olive tree (Olea europaea L. subsp. europaea) cultivated in Portugal revealed by RAPD and ISSR markers. Genet. Resour. Crop Evol. 51: 501–511. and
• Grzesiak, W. R. Lacroix, J. Wojcık and P. Błaszczyk 2003. A comparison of neural network and multiple regression predictions for 305-day lactation yield using partial lactation records. Can. J. Anim. Sci., 83 (2): 307-310.
• Heald, C.W., T. Kim, W.M. Sischo, J.B. Cooper and D.R. Wolfgang 2000. A computerized mastitis decision aid using farm-based records: an artificial neural network approach, J. Dairy Sci. 83 (2000), pp. 711–722.
• Hu, Y.H. and Hwang, J.N 2003. Handbook of neural network signal processing, Academic Press, London.
• Kaashoek, J.F., and H.K. van Dijk 2000. Neural Networks as econometric tool. Econemetric Institute Rapport EI2000-31A. Economic Institute, Erasmus University Rotterdam. Rotterdam, 29 pp.
• Kaul, M., R.L. Hill and C. Walthall 2005. Artificial neural networks for corn and soybean yield prediction. Agriculture Systems, 85(1), 1-18.
• Kayabaş, İ. ve H. Yıldırım 2008. Yapay sinir ağlarının uzaktan eğitimde destek için kullanımı. Anadolu Üniversitesi 8th International Educational Technology Conference. 542-545.
• Kuan, C.M. and H. White 1994. Artificial neural networks: An econometric perspective. Econometric Reviews., 13, 1-92.
• Kominakis, A.P., Abas, Z., Maltaris, I., and Rogdakis, E 2002. A preliminary study of the application of artificial neural networks to prediction of milk yield in dairy sheep. Computers and Electronics in Agriculture., 35(1), 35-48.
• Kirby, Y. K., R. W. McNew, J. D. Kirby, and R. F. Wideman 1997. Evaluation of logistic versus linear regression models for predicting pulmonary hypertension syndrome (ascites) using cold exposure or pulmonary artery clamp models in broilers. Poult. Sci., 76:392– 399.
• La Rocca, M. and C. Perna. 2005. Variable selection in neural network regression models with dependent data: a subsampling approach. Computational Statistics & Data Analysis., 48(2), 415-429.
• Lee, C.C, P.C. Chung, J.R. Tsai, C.I. Chang. 1999. Robust. Radial basis function neural networks. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on. IEEE Xplore 1999; 29(6):674-85.
• Miller, J 2003. Evolving developmental programs for adaptation, morphogenesys, and self-repair. W. Banzhaf, J. Ziegler and T. Christaller, Editors, Proceedings of the European congress of artificial life, ECAL. pp. 256–265.
• Mancuso, S. and F.P. Nicese 1999. Identifying olive (Olea europaea) cultivars using artificial neural networks. J. Am. Soc. Hort. Sci., 124, pp. 527–531.
• Owen, C.A, E.C. Bita, G. Banilas, S.E Hajjar, V. Sellianakis, U. Aksoy, S. Hepaksoy, R. Chamoun, S.N Talhook, I. Metzidakis, P. Hatzopoulos, P. Kalaitzis 2005. AFLP reveals structural details of genetic diversity within cultivated olive germplasm from the Eastern Mediterranean. Theor. Appl. Genet., 110: 1169–1176.
• Öztemel, E 2006. Yapay Sinir Ağları, Papatya Yayıncılık, İstanbul, Eylül 2006.
• Öztemel, E 2003 a. Artificial Neural Networks. 1st ed. Papatya Yayıncılık, İstanbul. s.36.
• Öztemel, E 2003 b. Artificial Neural Networks. 1st ed. Papatya Yayıncılık, İstanbul. s.40.
• Park, J. and M. Schlag-Rey 2005. Frames of reference for saccadic command, tested by saccade collision in the supplementary eye field. J. Neurophysiol., 95, 159–170.
• Paquet, J., C. Lacroix and J. Thibault 2000. Modeling of pH and Acidity for Industrial Cheese Production. J. Dairy Sci., 83 (11): 2393-2409.
• Poldaru, R., J. Roots, and A. H. Vira 2005. Artificial neural network as an alternative to multiple regression analysis for estimating the parameters for econometric models. Agronomy Research., 3(2), 177-187.
• Poldaru, R., and J. Roots 2003. The estimation of the econometric model of grain yield in Estonian Counties using neural Networks. VAGOS. 57(10): 124-130.
• Roush, W.B., Y.K. Kirby and T.L. Cravener and R.F. Wideman. 1996. Artificial neural network prediction of ascites in broilers. Poult. Sci., 75 (12):1479-1487
• Roush, W.B., T.L. Cravener, Y.K. Kirby and R.F. Wideman 1997. Probabilistic neural network prediction of ascites in broilers based on minimally invasive physiological factors. Poult. Sci., 76 (11):1513-1516.
• Roush, W.B., W.A Dozier and S.L. Branton 2006. Comparison of gompertz and neural network models of broiler growth. Poult. Sci., 85 (4): 794-797.
• Ruanet, V.V., A.M. Kudryavtsev and S.Y. Dadashev 2001. The use of artificial neural networks for automatic analysis and genetic identification of gliadin electrophoretic spectra in durum wheat. Russ. J. Genet., 37(10): 1435-1437.
• Ruanet V.V., E.Z. Kochieva and N.N Ryzhova 2005. Kohonen network study of the results of RAPD and ISSR analyses of genomic polymorphism in the genus Capsicum L. Russ. J. Genet., 4: 202–210.
• Rugini, E. and S. Lavee 1992. Olive in: Biotechnology of perennial fruit crops, Eds. Hammerchlag FA and Litz RE. CAB International, pp. 371-382.
• Salle, C.T.P, A.S. Guahyba, V.B. Wald, A.B. Silva, F.O. Salle and V.P. Nascemento 2003. Use of artificial neural networks to estimate production parameters of broilers breeders in the production phase. Br. Poult. Sci., 44 (2): 211–217.
• Takcı, H 2008. GYTE, Bilgisayar Mühendisliği Bölümü, BIL 482- BIL http://www.bilmuh.gyte.edu.tr/BIL482/ . Ağları Ders Sunumu.
• Trujillo, I., L. Rallo, E.A. Carbonell and M.J. Asins 1990. Isoenzymatic variability of olive cultivars according to their origin. Acta. Hort., 286: 137-140.
• Uno, Y., S.O. Prasher, R. Lacrois, P.K Goel, Y. Karimi, A. Viau, R.M Patel 2005. Artificial neural networks to predict corn yield from Compact Airbone Spectrogaphic Imager data. Computers and Electronics in Agriculture. 47(2), 149-161.
• Yang, C.C., S.O. Prasher, J.A Landry and H.S Ramaswamy 2003. Development of a herbicide application map using artificial neural Networks and fuzzy logic. Agricultural Systems 76(2), 561-574.
• Yee, D., M.G Prior and L.Z Florence 1993. Development of predictive models of laboratory animal growth using artificial neural networks. Oxford Journals., 9 (5): 517-522.
• Zhang, G.P 2007. A neural network ensemble method with jittered training data for time series forecasting. Information Sciences. 177: 5329-5346.