An Artificial Neural Network for classifying and predicting soil moisture and temperature using Levenberg-Marquardt algorithm

被引：9

作者：

Atluri, V ^{[1
]}

Hung, CC ^{[1
]}

Coleman, TL ^{[1
]}

机构：

[1] Alabama A&M Univ, Dept Math & Comp Sci, Normal, AL 35762 USA

来源：

IEEE SOUTHEASTCON '99, PROCEEDINGS | 1999年

关键词：

D O I：

10.1109/SECON.1999.766079

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The purpose of this study was to design an artificial neural network that classifies soils and quantitatively predict the soil moisture and temperature in a given soil type based on the remotely sensed data. Two different training algorithms, viz,, backpropagation (BP) and Levenberg-Marquardt (LM), were employed, The accuracy of the networks studied ranged from 96.68 to 98.8%, Networks trained with LM algorithm were faster. It is concluded that neural networks can be used as a paradigm in soil classification as well as in predicting the quantity of soil moisture and temperature accurately, using remotely sensed micron ave data, and thus helps achieve a proper crop management.

引用

页码：10 / 13

页数：4

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