NDLI: Application of Artificial Neural Network on Relationship Analysis between Wheat Yield and Soil Nutrients

Content Provider	IEEE Xplore Digital Library
Author	Yong He Yun Zhang Shujuan Zhang Hui Fang
Copyright Year	2005
Description	Author affiliation: Coll. of Biosyst. Eng. & Food Sci., Zhejiang Univ., Hangzhou (Yong He; Yun Zhang; Shujuan Zhang; Hui Fang)
Abstract	As a type of nonlinear dynamic parallelism system, artificial neutral network has a capacity of identifying complex causal relationships between variables, such as those between the winter wheat yield and the soil spatial distribution nutrients, including water content, organic matter, total nitrogen, alkali-hydrolysable nitrogen, rapidly available phosphorus and potassium. By training 50 tested soil samples in the back-propagation neural network of topological structure 6:9:1, the model of analyzing the relation between the crop yield and those 6 soil characteristics was established to validate the remaining 13 samples. The results show that the soil water content and alkali-hydrolysable nitrogen are linear to the crop yield, the total nitrogen, organic matter and rapidly available potassium are respectively multinomial to it and that the rapidly available phosphorous is of the exponential relationship with the crop yield
Starting Page	4530
Ending Page	4533
File Size	222781
Page Count	4
File Format	PDF
ISBN	0780387414
DOI	10.1109/IEMBS.2005.1615476
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Soil Crops Nitrogen Agricultural engineering Predictive models Nonlinear dynamical systems Parameter estimation Educational programs Neural networks
Content Type	Text
Resource Type	Article

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