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Varietal Classification of Lactuca Sativa Seeds Using an Adaptive Neuro-Fuzzy Inference System Based on Morphological Phenes
Seed varieties are often differentiated via the manual and subjective classification of their external textural, spectral, and morphological biosignatures. This traditional method of manually inspecting seeds is inefficient and unreliable for seed phenotyping. The application of computer vision is a...
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| Published in: | Journal of advanced computational intelligence and intelligent informatics 2021-09, Vol.25 (5), p.618-624 |
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| Main Authors: | , , , , , , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c469t-f416c41dd6211ae1c6c35afdd9317f93070feb86196d5821d93248cc2c5a24f3 |
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| cites | cdi_FETCH-LOGICAL-c469t-f416c41dd6211ae1c6c35afdd9317f93070feb86196d5821d93248cc2c5a24f3 |
| container_end_page | 624 |
| container_issue | 5 |
| container_start_page | 618 |
| container_title | Journal of advanced computational intelligence and intelligent informatics |
| container_volume | 25 |
| creator | Mendigoria, Christan Hail R. Aquino, Heinrick L. Alajas, Oliver John Y. II, Ronnie S. Concepcion Dadios, Elmer P. Sybingco, Edwin Bandala, Argel A. Vicerra, Ryan Rhay P. |
| description | Seed varieties are often differentiated via the manual and subjective classification of their external textural, spectral, and morphological biosignatures. This traditional method of manually inspecting seeds is inefficient and unreliable for seed phenotyping. The application of computer vision is an ideal solution allied with computational intelligence. This study used
Lactuca sativa
seed variants, which are commercially known as grand rapid, Chinese loose-leaf, and iceberg (which serves as noise data for extended model evaluation), in determining their corresponding classifications based on the extended morphological phenes using computational intelligence. Red-green-blue (RGB) imaging was employed for individual kernels. Extended morphological phenes, that is, solidity, roundness, compactness, and shape factors, were computed based on seed architectural traits and used as predictors to discriminate among the three cultivars. The suitability of ANFIS, NB, and CT was explored using a limited dataset. A mean accuracy of 100% was manifested in ANFIS; thus, it was proved to be the most reliable model. |
| doi_str_mv | 10.20965/jaciii.2021.p0618 |
| format | article |
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Lactuca sativa
seed variants, which are commercially known as grand rapid, Chinese loose-leaf, and iceberg (which serves as noise data for extended model evaluation), in determining their corresponding classifications based on the extended morphological phenes using computational intelligence. Red-green-blue (RGB) imaging was employed for individual kernels. Extended morphological phenes, that is, solidity, roundness, compactness, and shape factors, were computed based on seed architectural traits and used as predictors to discriminate among the three cultivars. The suitability of ANFIS, NB, and CT was explored using a limited dataset. A mean accuracy of 100% was manifested in ANFIS; thus, it was proved to be the most reliable model.</description><identifier>ISSN: 1343-0130</identifier><identifier>EISSN: 1883-8014</identifier><identifier>DOI: 10.20965/jaciii.2021.p0618</identifier><language>eng</language><publisher>Tokyo: Fuji Technology Press Co. Ltd</publisher><subject>Adaptive systems ; Artificial intelligence ; Artificial neural networks ; Classification ; Computer vision ; Fuzzy logic ; Icebergs ; Morphology ; Roundness ; Seeds ; Shape factor</subject><ispartof>Journal of advanced computational intelligence and intelligent informatics, 2021-09, Vol.25 (5), p.618-624</ispartof><rights>Copyright © 2021 Fuji Technology Press Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c469t-f416c41dd6211ae1c6c35afdd9317f93070feb86196d5821d93248cc2c5a24f3</citedby><cites>FETCH-LOGICAL-c469t-f416c41dd6211ae1c6c35afdd9317f93070feb86196d5821d93248cc2c5a24f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,786,790,870,27957,27958</link.rule.ids></links><search><creatorcontrib>Mendigoria, Christan Hail R.</creatorcontrib><creatorcontrib>Aquino, Heinrick L.</creatorcontrib><creatorcontrib>Alajas, Oliver John Y.</creatorcontrib><creatorcontrib>II, Ronnie S. Concepcion</creatorcontrib><creatorcontrib>Dadios, Elmer P.</creatorcontrib><creatorcontrib>Sybingco, Edwin</creatorcontrib><creatorcontrib>Bandala, Argel A.</creatorcontrib><creatorcontrib>Vicerra, Ryan Rhay P.</creatorcontrib><creatorcontrib>Manufacturing Engineering and Management Department, De La Salle University 2401 Taft Ave, Malate, Manila 1004, Philippines</creatorcontrib><creatorcontrib>Electronics and Communications Engineering Department, De La Salle University 2401 Taft Ave, Malate, Manila 1004, Philippines</creatorcontrib><title>Varietal Classification of Lactuca Sativa Seeds Using an Adaptive Neuro-Fuzzy Inference System Based on Morphological Phenes</title><title>Journal of advanced computational intelligence and intelligent informatics</title><description>Seed varieties are often differentiated via the manual and subjective classification of their external textural, spectral, and morphological biosignatures. This traditional method of manually inspecting seeds is inefficient and unreliable for seed phenotyping. The application of computer vision is an ideal solution allied with computational intelligence. This study used
Lactuca sativa
seed variants, which are commercially known as grand rapid, Chinese loose-leaf, and iceberg (which serves as noise data for extended model evaluation), in determining their corresponding classifications based on the extended morphological phenes using computational intelligence. Red-green-blue (RGB) imaging was employed for individual kernels. Extended morphological phenes, that is, solidity, roundness, compactness, and shape factors, were computed based on seed architectural traits and used as predictors to discriminate among the three cultivars. The suitability of ANFIS, NB, and CT was explored using a limited dataset. A mean accuracy of 100% was manifested in ANFIS; thus, it was proved to be the most reliable model.</description><subject>Adaptive systems</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Classification</subject><subject>Computer vision</subject><subject>Fuzzy logic</subject><subject>Icebergs</subject><subject>Morphology</subject><subject>Roundness</subject><subject>Seeds</subject><subject>Shape factor</subject><issn>1343-0130</issn><issn>1883-8014</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNotkEtLAzEUhYMoWLR_wFXA9dS8Jp1Z1mK1UB_Q6jbEzE2bMp2MyYzQ4o83tq7uuYfDOfAhdEPJiJFS5ndbbZxz6WF01BJJizM0oEXBs4JQcZ40FzwjlJNLNIxxS0jSTBJBB-jnQwcHna7xtNYxOuuM7pxvsLd4oU3XG42XyflOB6CK-D26Zo11gyeVbpMP-AX64LNZfzjs8byxEKAxgJf72MEO3-sIFU59zz60G1_7dRqo8dsGGojX6MLqOsLw_16h1exhNX3KFq-P8-lkkRkhyy6zgkojaFVJRqkGaqThubZVVXI6tiUnY2Lhs5C0lFVeMJp8JgpjmMk1E5ZfodtTbRv8Vw-xU1vfhyYtKpaPBUkQS5FS7JQywccYwKo2uJ0Oe0WJOnJWJ87qj7M6cua_jsVzOQ</recordid><startdate>20210901</startdate><enddate>20210901</enddate><creator>Mendigoria, Christan Hail R.</creator><creator>Aquino, Heinrick L.</creator><creator>Alajas, Oliver John Y.</creator><creator>II, Ronnie S. 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This traditional method of manually inspecting seeds is inefficient and unreliable for seed phenotyping. The application of computer vision is an ideal solution allied with computational intelligence. This study used
Lactuca sativa
seed variants, which are commercially known as grand rapid, Chinese loose-leaf, and iceberg (which serves as noise data for extended model evaluation), in determining their corresponding classifications based on the extended morphological phenes using computational intelligence. Red-green-blue (RGB) imaging was employed for individual kernels. Extended morphological phenes, that is, solidity, roundness, compactness, and shape factors, were computed based on seed architectural traits and used as predictors to discriminate among the three cultivars. The suitability of ANFIS, NB, and CT was explored using a limited dataset. A mean accuracy of 100% was manifested in ANFIS; thus, it was proved to be the most reliable model.</abstract><cop>Tokyo</cop><pub>Fuji Technology Press Co. Ltd</pub><doi>10.20965/jaciii.2021.p0618</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | Journal of advanced computational intelligence and intelligent informatics, 2021-09, Vol.25 (5), p.618-624 |
| issn | 1343-0130 1883-8014 |
| language | eng |
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| source | DOAJ Directory of Open Access Journals |
| subjects | Adaptive systems Artificial intelligence Artificial neural networks Classification Computer vision Fuzzy logic Icebergs Morphology Roundness Seeds Shape factor |
| title | Varietal Classification of Lactuca Sativa Seeds Using an Adaptive Neuro-Fuzzy Inference System Based on Morphological Phenes |
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