Development of an intelligent prediction tool for rice yield based on machine learning techniques

Intelligent systems based on machine learning techniques. such as classification. clustering. are gaining Wide spread popularity in real world applications. This paper presents work on developing a software system for predicting rice yield from climate and plantation data. In this work. the main foc...

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Main Authors: Md. Sap, Mohd. Noor, Awan, A. M.
Format: Article
Language:English
Published: Penerbit UTM Press 2006
Subjects:
Online Access:http://eprints.utm.my/8203/1/MohdNoorMd_DevelopmentofanIntelligentPredictionToolforRice2006.pdf
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author Md. Sap, Mohd. Noor
Awan, A. M.
author_facet Md. Sap, Mohd. Noor
Awan, A. M.
author_sort Md. Sap, Mohd. Noor
collection ePrints
description Intelligent systems based on machine learning techniques. such as classification. clustering. are gaining Wide spread popularity in real world applications. This paper presents work on developing a software system for predicting rice yield from climate and plantation data. In this work. the main focu s is on classification and clustering techniques for data analysis based on statistical and machine learning approaches. Support vector machine algorithm is developed for classification of rice plantation data. Whereas kernel-based clustering algorithm is developed for finding clusters in climate data. The values of these parameters at various points oftime constitute time series. As the next step, correlation and regression analysis is applied for analyzing the impact of various parameters on the rice yield. and also for predicting the yield.
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spelling utm.eprints-82032017-11-01T04:17:28Z http://eprints.utm.my/8203/ Development of an intelligent prediction tool for rice yield based on machine learning techniques Md. Sap, Mohd. Noor Awan, A. M. QA75 Electronic computers. Computer science Intelligent systems based on machine learning techniques. such as classification. clustering. are gaining Wide spread popularity in real world applications. This paper presents work on developing a software system for predicting rice yield from climate and plantation data. In this work. the main focu s is on classification and clustering techniques for data analysis based on statistical and machine learning approaches. Support vector machine algorithm is developed for classification of rice plantation data. Whereas kernel-based clustering algorithm is developed for finding clusters in climate data. The values of these parameters at various points oftime constitute time series. As the next step, correlation and regression analysis is applied for analyzing the impact of various parameters on the rice yield. and also for predicting the yield. Penerbit UTM Press 2006-12 Article PeerReviewed application/pdf en http://eprints.utm.my/8203/1/MohdNoorMd_DevelopmentofanIntelligentPredictionToolforRice2006.pdf Md. Sap, Mohd. Noor and Awan, A. M. (2006) Development of an intelligent prediction tool for rice yield based on machine learning techniques. Jurnal Teknologi Maklumat, 18 (2). pp. 73-74. ISSN 0128-3790
spellingShingle QA75 Electronic computers. Computer science
Md. Sap, Mohd. Noor
Awan, A. M.
Development of an intelligent prediction tool for rice yield based on machine learning techniques
title Development of an intelligent prediction tool for rice yield based on machine learning techniques
title_full Development of an intelligent prediction tool for rice yield based on machine learning techniques
title_fullStr Development of an intelligent prediction tool for rice yield based on machine learning techniques
title_full_unstemmed Development of an intelligent prediction tool for rice yield based on machine learning techniques
title_short Development of an intelligent prediction tool for rice yield based on machine learning techniques
title_sort development of an intelligent prediction tool for rice yield based on machine learning techniques
topic QA75 Electronic computers. Computer science
url http://eprints.utm.my/8203/1/MohdNoorMd_DevelopmentofanIntelligentPredictionToolforRice2006.pdf
work_keys_str_mv AT mdsapmohdnoor developmentofanintelligentpredictiontoolforriceyieldbasedonmachinelearningtechniques
AT awanam developmentofanintelligentpredictiontoolforriceyieldbasedonmachinelearningtechniques