Comparison of pyrus fruit classification using K-Nearest Neighbor (KNN) and Adaptive Neuro Fuzzy Inference System (ANFIS) algorithms
Pears or Pyrus fruits are popular among the public because of their high nutritional content, delicious taste and low calories. Fruit classification can be done visually, but manual classification requires consistent techniques and is often constrained by human aspects. Digital image processing is a...
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| creator | Hasanah, Riyan Latifahul Indarti Laraswati, Dewi Marlina Priadi, Agus |
| description | Pears or Pyrus fruits are popular among the public because of their high nutritional content, delicious taste and low calories. Fruit classification can be done visually, but manual classification requires consistent techniques and is often constrained by human aspects. Digital image processing is applied to solve the above problems. The pears identified in this study came from three types, namely the Abate Pear, the Monster Pear and the William Pear. Pre-processing is done by converting the RGB image to L*a*b, then segmenting it using the K-Means Clustering algorithm. The segmented image is extracted into 7 features, namely 6 color features (Red, Green, Blue, Hue, Saturation, Value) and 1 size feature (Area). Classification is carried out using the K-Nearest Neighbor (KNN) algorithm and the Adaptive Neuro Fuzzy Inference System (ANFIS). The results showed that the KNN algorithm had a better performance in classifying the types of pears. |
| doi_str_mv | 10.1063/5.0128365 |
| format | Conference Proceeding |
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Fruit classification can be done visually, but manual classification requires consistent techniques and is often constrained by human aspects. Digital image processing is applied to solve the above problems. The pears identified in this study came from three types, namely the Abate Pear, the Monster Pear and the William Pear. Pre-processing is done by converting the RGB image to L*a*b, then segmenting it using the K-Means Clustering algorithm. The segmented image is extracted into 7 features, namely 6 color features (Red, Green, Blue, Hue, Saturation, Value) and 1 size feature (Area). Classification is carried out using the K-Nearest Neighbor (KNN) algorithm and the Adaptive Neuro Fuzzy Inference System (ANFIS). The results showed that the KNN algorithm had a better performance in classifying the types of pears.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0128365</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Adaptive algorithms ; Adaptive systems ; Artificial neural networks ; Cluster analysis ; Clustering ; Digital imaging ; Fuzzy logic ; Image processing ; Inference ; K-nearest neighbors algorithm ; Pears ; Saturation (color) ; Vector quantization</subject><ispartof>AIP conference proceedings, 2023, Vol.2714 (1)</ispartof><rights>Author(s)</rights><rights>2023 Author(s). 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Fruit classification can be done visually, but manual classification requires consistent techniques and is often constrained by human aspects. Digital image processing is applied to solve the above problems. The pears identified in this study came from three types, namely the Abate Pear, the Monster Pear and the William Pear. Pre-processing is done by converting the RGB image to L*a*b, then segmenting it using the K-Means Clustering algorithm. The segmented image is extracted into 7 features, namely 6 color features (Red, Green, Blue, Hue, Saturation, Value) and 1 size feature (Area). Classification is carried out using the K-Nearest Neighbor (KNN) algorithm and the Adaptive Neuro Fuzzy Inference System (ANFIS). The results showed that the KNN algorithm had a better performance in classifying the types of pears.</description><subject>Adaptive algorithms</subject><subject>Adaptive systems</subject><subject>Artificial neural networks</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Digital imaging</subject><subject>Fuzzy logic</subject><subject>Image processing</subject><subject>Inference</subject><subject>K-nearest neighbors algorithm</subject><subject>Pears</subject><subject>Saturation (color)</subject><subject>Vector quantization</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2023</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNp9kMFLwzAYxYMoOKcH_4OAlyl0Jk2aZMcxnI6NepiCt5I2yZaxNTVpB93ZP9zOCd48fYf3e-_jPQBuMRpixMhjMkQ4FoQlZ6CHkwRHnGF2DnoIjWgUU_JxCa5C2CAUjzgXPfA1cbtKehtcCZ2BVeubAI1vbA2LrQzBGlvI2nZqE2y5gvMo1dLrUMNU29U6dx4O5ml6D2Wp4FjJqrZ73WmNd3DaHA4tnJVGe10WGi7bUOsdHIzT6WzZObYr52293oVrcGHkNuib39sH79Ont8lLtHh9nk3Gi6jCTNSRELnRSmsudVeHJIQpxjmiNFc0loJwxVVMC0oNHhku85zmgnTOJC4UlwyTPrg75VbefTZdiWzjGl92L7NYYExiRJMj9XCiQmHrn-5Z5e1O-jbDKDuunCXZ78r_wXvn_8CsUoZ8A28YffQ</recordid><startdate>20230509</startdate><enddate>20230509</enddate><creator>Hasanah, Riyan Latifahul</creator><creator>Indarti</creator><creator>Laraswati, Dewi</creator><creator>Marlina</creator><creator>Priadi, Agus</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20230509</creationdate><title>Comparison of pyrus fruit classification using K-Nearest Neighbor (KNN) and Adaptive Neuro Fuzzy Inference System (ANFIS) algorithms</title><author>Hasanah, Riyan Latifahul ; Indarti ; Laraswati, Dewi ; Marlina ; Priadi, Agus</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p168t-88bfedee7ae0093536d677044bd42a837d7d24c44f19f7abb4b8316852cd7a613</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Adaptive algorithms</topic><topic>Adaptive systems</topic><topic>Artificial neural networks</topic><topic>Cluster analysis</topic><topic>Clustering</topic><topic>Digital imaging</topic><topic>Fuzzy logic</topic><topic>Image processing</topic><topic>Inference</topic><topic>K-nearest neighbors algorithm</topic><topic>Pears</topic><topic>Saturation (color)</topic><topic>Vector quantization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hasanah, Riyan Latifahul</creatorcontrib><creatorcontrib>Indarti</creatorcontrib><creatorcontrib>Laraswati, Dewi</creatorcontrib><creatorcontrib>Marlina</creatorcontrib><creatorcontrib>Priadi, Agus</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hasanah, Riyan Latifahul</au><au>Indarti</au><au>Laraswati, Dewi</au><au>Marlina</au><au>Priadi, Agus</au><au>Junaidi, Agus</au><au>Agustiani, Sarifah</au><au>Arifin, Yoseph Tajul</au><au>Baidawi, Taufik</au><au>Dalis, Sopiyan</au><au>Haryani</au><au>Hastuti, Dwi Puji</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Comparison of pyrus fruit classification using K-Nearest Neighbor (KNN) and Adaptive Neuro Fuzzy Inference System (ANFIS) algorithms</atitle><btitle>AIP conference proceedings</btitle><date>2023-05-09</date><risdate>2023</risdate><volume>2714</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>Pears or Pyrus fruits are popular among the public because of their high nutritional content, delicious taste and low calories. Fruit classification can be done visually, but manual classification requires consistent techniques and is often constrained by human aspects. Digital image processing is applied to solve the above problems. The pears identified in this study came from three types, namely the Abate Pear, the Monster Pear and the William Pear. Pre-processing is done by converting the RGB image to L*a*b, then segmenting it using the K-Means Clustering algorithm. The segmented image is extracted into 7 features, namely 6 color features (Red, Green, Blue, Hue, Saturation, Value) and 1 size feature (Area). Classification is carried out using the K-Nearest Neighbor (KNN) algorithm and the Adaptive Neuro Fuzzy Inference System (ANFIS). The results showed that the KNN algorithm had a better performance in classifying the types of pears.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0128365</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP conference proceedings, 2023, Vol.2714 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
| recordid | cdi_scitation_primary_10_1063_5_0128365 |
| source | AIP Journals Complete |
| subjects | Adaptive algorithms Adaptive systems Artificial neural networks Cluster analysis Clustering Digital imaging Fuzzy logic Image processing Inference K-nearest neighbors algorithm Pears Saturation (color) Vector quantization |
| title | Comparison of pyrus fruit classification using K-Nearest Neighbor (KNN) and Adaptive Neuro Fuzzy Inference System (ANFIS) algorithms |
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