期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2018
卷号：9
期号：8
DOI：10.14569/IJACSA.2018.090838
出版社：Science and Information Society (SAI)
摘要：Wheat has been a prime source of food for the mankind for centuries. The final wheat grain yield is the multitude of the complex interaction among the various yield attributes such as kernel per plant, Spike per plant, NSpt/s, Spike Dry Weight (SDW), etc. Different approaches have been followed to understand the non-linear relationship between the attributes and the yield to manage the crop better in the context of precision agriculture. In this study, Principle Component analysis (PCA) and Stepwise regression used to reduce the dimension of the original data to get the critical attributes under study. The reduced dataset is then modeled using the Radial Basis neural network. RBNN provides the regression value more than 0.95 which indicates the strong dependence of the yield on the critical traits.
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