Neural-Discrete Hungry Roach Infestation Optimization to Select Informative Textural Features for Determining Water Content of Cultured Sunagoke Moss
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- HENDRAWAN Yusuf
- Bio-instrumentation, Control and Systems (BICS) Engineering Laboratory, Graduate School of Life and Environmental Sciences, Osaka Prefecture University
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- MURASE Haruhiko
- Bio-instrumentation, Control and Systems (BICS) Engineering Laboratory, Graduate School of Life and Environmental Sciences, Osaka Prefecture University
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In this paper, a method to extract textural features from Colour Co-occurrence Matrix (CCM) and also a method to select relevant textural features for predicting water content of Sunagoke moss (Rhacomitrium japonicum) are proposed. The aim of this paper is to construct machine vision-based precision irrigation system. The objective of this paper is to propose Neural-Discrete Hungry Roach Infestation Optimization (N-DHRIO) algorithm to find the most significant set of textural features suitable for predicting water content of cultured Sunagoke moss using machine vision. N-DHRIO is an optimization algorithm for feature selection that is inspired by the social behaviour of cockroaches. The performance of the proposed feature selection method here was compared with Neural-Genetic Algorithms (N-GAs), Neural-Discrete Particle Swarm Optimization (N-DPSO) and Neural-Simulated Annealing (N-SA). Textural features consisted of 120 textural features extracted from gray, RGB, HSV, HSL and L*a*b* colour spaces. Non-linear relationships between textural features and water content were identified by Back-Propagation Neural Network (BPNN). The results showed significant statistical improvement between methods using feature selection and methods without feature selection. Experimental results also indicated the superiority of N-DHRIO among other feature selection methods, since it achieved better prediction performance as the objective of this research.
収録刊行物
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- Environment Control in Biology
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Environment Control in Biology 49 (1), 1-21, 2011
日本生物環境工学会
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詳細情報 詳細情報について
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- CRID
- 1390282680237472128
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- NII論文ID
- 10028262958
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- NII書誌ID
- AA12029220
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- DOI
- 10.2525/ecb.49.1
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- ISSN
- 18830986
- 1880554X
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- NDL書誌ID
- 11073060
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可