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

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3 Author(s)
Atluri, V. ; Dept. of Math. & Comput. Sci., Alabama A&M Univ., Normal, AL, USA ; Chih-Cheng Hung ; Coleman, T.L.

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

Published in:

Southeastcon '99. Proceedings. IEEE

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