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Title of the item:

Artificial Neural Network for Classification and Analysis of Degraded Soils

Title :
Artificial Neural Network for Classification and Analysis of Degraded Soils
Authors :
Bonini Neto, A. [UNESP]
Bonini, C. S.B. [UNESP]
Bisi, B. S. [UNESP]
Coletta, L. F.S. [UNESP]
Dos Reis, A. R. [UNESP]
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Subject Terms :
Intelligent systems
Recovery of soil
Soil physics
Artificial intelligence
Source :
Repositório Institucional da UNESP
Universidade Estadual Paulista
Publisher :
Ieee-inst Electrical Electronics Engineers Inc, 2017.
Publication Year :
2017
Language :
Portuguese
DOI :
10.1109/TLA.2017.7867601
Accession Number :
edsair.dedup.wf.001..9cf592c725e47e631c84a25c57b5e530
Made available in DSpace on 2018-12-11T16:46:23Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-03-01 This study aimed to evaluate the Artificial Neural Network (ANN) to establish a classification and analysis of degraded soils and its recovery in response to lime and gypsum application. The analyzed degraded soil was classified as Oxisol, and the physical attributes considered were: soil density, soil porosity (macroporosity and microporosity) and soil penetration resistance. The ANN used in this study is the backpropagation composed of two layers, the middle layer and the output layer, with supervised training. The network has four inputs, that are the physical attributes of the soil, in the middle layer the network contains ten neurons and the output layer only one neuron, which has the function of informing if the soil was recovered (R), partially recovered (PR) or not recovered (NR). The analyzed data come from the year 2012, concerning the depths 0.0-0.1 m, 0.1-0.2 m and 0.2-0.4 m. Considering the performance of ANN, it was verified that the network obtained an adequate training to classify the degraded soils, showing low mean square error of analyzed data. Therefore, ANN is considered an interesting alternative and a powerful automatic tool to classify degraded soils during recovery process. Faculdade de Ciências e Engenharia UNESP Faculdade de Ciências Agronômicas e Tecnológicas UNESP Faculdade de Ciências e Engenharia UNESP Faculdade de Ciências Agronômicas e Tecnológicas UNESP

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