An artificial neural network for classifying and predicting soil moisture and temperature using Levenberg-Marquardt algorithm

Author

Atluri, Venkata ; Hung, Chih-Cheng ; Coleman, Tommy L.

Author_Institution

Dept. of Math. & Comput. Sci., Alabama A&M Univ., Normal, AL, USA

fYear

1999

fDate

1999

Firstpage

10

Lastpage

13

Abstract

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%. The 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 microwave data, and thus helps achieve a proper crop management

Keywords

agriculture; backpropagation; computerised monitoring; feedforward neural nets; pattern classification; soil; Levenberg-Marquardt algorithm; agriculture; backpropagation; crop management; feedforward neural network; soil classification; soil moisture; soil temperature; Artificial neural networks; Backpropagation algorithms; Crops; Hydrology; Moisture measurement; Remote sensing; Soil measurements; Soil moisture; Temperature measurement; Temperature sensors;

fLanguage

English

Publisher

ieee

Conference_Titel

Southeastcon '99. Proceedings. IEEE

Conference_Location

Lexington, KY

Print_ISBN

0-7803-5237-8

Type

conf

DOI

10.1109/SECON.1999.766079

Filename

766079