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  1. University of Computer Studies, Yangon
  2. Conferences

Neural Approach Applying for Soil Classification System

http://hdl.handle.net/20.500.12678/0000004121
http://hdl.handle.net/20.500.12678/0000004121
0c57987d-7a91-42ae-b40c-5a7249f4a49e
b69423dc-fd00-4d1a-98d8-25627d8c661d
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55244.pdf 55244.pdf (461 Kb)
Publication type
Article
Upload type
Publication
Title
Title Neural Approach Applying for Soil Classification System
Language en
Publication date 2009-12-30
Authors
Sue, Khin Mo
Kyi, Tin Mar
Description
The main aim of this paper is to develop a system for classifying the soil class of Myanmar by the help of Error Back-propagation algorithm (EBP) which is the most widely used algorithm among Artificial Neural Network (ANN ) technique. This system, “Soil Classifier” includes two parts in general, training and testing. During the training phase, the Soil Classifier accepts nine inputs. These inputs are soil type, land use type, land form, soil depth, soil texture, soil PH and the percentages of each of three types of plant nutrients. The plant nutrients are Nitrogen ( N ), Phosphorus ( P ) and Potassium ( K ). After accepting the nine inputs, the Soil Classifier will generate one of three types of outputs whether the soil is good class, fair class or poor class. The Soil Classifier uses Multi Layer Feed-forward Neural Network.
Keywords
Error Back-propagation algorithm, Artificial Neural Network (ANN), Multilayer Feed-forward Neural Network
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1822
Journal articles
Fourth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
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