Yapay Sinir Ağları ve Tarım Alanındaki Uygulamaları

Öz

Yapay Sinir Ağları (YSA), bir veri giriş sistemidir. Bu sistemdeki kurallar ve ilişkiler tam olarak bilinmemektedir. Bu kuralları ve ilişkileri ortaya çıkarmak için mevcut verilerden hareket edilerek bir veri işleme sistemi ve algoritması geliştirilir. YSA aynı zamanda günümüzde bir çok alanda kullanılmaya başlanan modern sezgisel algoritmalardandır. Bu çalışmada YSA metodu anlatılmış ve bu metodun çeşitli tarım alanlarında uygulamaları ele alınmıştır. Amaç, tarım alanında çalışan araştırmacıların ilgisini bu metoda çekmek ve tarımsal problemlerin çözümünde bir alternatif yöntem olarak dikkate alınmasını sağlamaktır.

Anahtar Kelimeler

• Yapay Sinir Ağları, Tarım

Kaynakça

1. Aitkenhead, M. J., Dalgetty, I. A., Mullins, C. E., McDonald, A. J. S. and Strachan, N. J. S., 2003, “Weed and crop discrimination using image analysis and artificial intelligence methods”, Computers and Electronics in Agriculture, V: 39(3), p: 157-171.
2. Elizondo D., G.Hoogenboom and McClendon R.W.,1994, “Development of a Neural Network Model to Predict Daily Solar Radiation”, Agricultural and Forest Meteorology, V:71, pp.115-132.
3. Elizondo D.A., McClendon R.W. and Hoogenboom G., 1994, “Neural Network Models for Predicting Flowering and Physiological Maturity of Soybean”, Transactions of the ASAE, Vol.37(3), pp.981-988.
4. Haykin, S., 1994, Neural Networks, Maxwell-McMillan, Ontario- Canada.
5. Jarmulak, J., Spronck P.and Kerckhoffs E. J. H., 1997, “Neural networks in process control: model-based and reinforcement trained controllers”, Computers and Electronics in Agriculture, V: 18(2-3), p: 149-166.
6. McClendon R.W., Hoogenboom G. and Seginer I., 1996, “Optimal Control and Neural Networks Applied to Peanut Irrigation Management”, Transactions of the ASAE, Vol.39(1) pp.275- 279.
7. Moshou, D., Bravo, C., West, J., Wahlen, S., McCarney, A. and Ramon, H., 2004, “Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks”, Computers and Electronics in Agriculture, V: 44(3), p: 173- 188.
8. Moshou, D., Vrinds, E., Ketelaere, B. D., Baerdemaeker, J. D and Ramon, H., 2001, “A neural network based plant classifier”, Computers and Electronics in Agriculture, V: 31(1), p: 5-16.

9. Noguchi N and Terao H., 1997, “Path planning of an agricultural mobile robot by neural network and genetic algorithm”, Computers and Electronics in Agriculture, V:18(2-3), p: 187- 204.
10. Öztemel, E., 2003, Yapay Sinir Ağları, Papatya Yayıncılık, İstanbul.
11. Park, S. J., Hwang, C. S. and Vlek, P., L.,G., 2005, “Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions”, Agricultural Systems, V: 85(1), p: 59-81.
12. Parmar R.S. vd., 1997, ”Estimation of Aflatoxin Contamination in Preharvest Peanuts Using Neural Networks”, Transactions of the ASAE, Vol.40(3),pp.809-813.
13. Patel, V. C., McClendon R. W. and Goodrum J. W., 1998, “Development and evaluation of an expert system for egg sorting”, Computers and Electronics in Agriculture, V: 20(2), p: 97-116.
14. Sharma, V., Negi, S. C., Rudra, R. P. and Yang, S., 2003, “Neural networks for predicting nitrate-nitrogen in drainage water”, Agricultural Water Management, V: 63(3), p: 169-183.
15. Tamari S., Ruiz-Sudrez J.C. and Wösten J.H.M., 1996, “Testing an Artificial Neural Network for Predicting Soil Hydraulic Conductivity”, Proceedings of 6thIntern.Conf. on Computers in Agriculture, Mexico, pp.912-919.
16. Torii, T., 2000, “Research in autonomous agriculture vehicles in Japan”, Computers and Electronics in Agriculture, V: 25(1- 2), p:133-153.
17. Uno, Y., Prasher, S. O., Lacroix, R., Goel, P. K., Karimi, Y., Viau, A. and Patel, R. M., 2005, “Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data”, Computers and Electronics in Agriculture, V: 47(2), p: 149-161.
18. Williams D.B. and Zazueta F.S., 1996 “Solar Radiation Estimation via Neural Network”, Proceedings of 6thIntern.Conf. on Computers in Agriculture, Mexico, pp.1143-1149.
19. Yang C.-C., Lacroix R. and Prasher S.O., 1996, “The Use of Back- Propagation Neural Network for the Simulation and Analyses of Time Series Data in Subsurface Drainage Systems”, Proceedings of 6th Intern.Conf. on Computers in Agriculture, Mexico, pp.941-949.
20. Yang C.-C., Prasher S.O. and Lacroix R., 1996, Application of Artificial Neural Networks in Subsurface Drainage System, Proceedings of 6th Intern.Conf. on Computers in Agriculture, Mexico, pp.932-940.
21. Yang, C.C., Prasher, S. O., Landry, J. A. and Ramaswamy, H. S., 2003,
22. “Development of a herbicide application
23. map using artificial neural networks and fuzzy
24. logic”, Agricultural Systems, V: 76(2), p:561-574.
25. Zurada, J. M., 1992, Introduction Artificial Neural Systems, West Publishing Company, St. Paul.