{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T07:22:16Z","timestamp":1773559336430,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2012,10,22]],"date-time":"2012-10-22T00:00:00Z","timestamp":1350864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category.<\/jats:p>","DOI":"10.3390\/s121014179","type":"journal-article","created":{"date-parts":[[2012,10,22]],"date-time":"2012-10-22T11:27:55Z","timestamp":1350905275000},"page":"14179-14195","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch"],"prefix":"10.3390","volume":"12","author":[{"given":"Norasyikin","family":"Fadilah","sequence":"first","affiliation":[{"name":"School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junita","family":"Mohamad-Saleh","sequence":"additional","affiliation":[{"name":"School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zaini","family":"Abdul Halim","sequence":"additional","affiliation":[{"name":"Collaborative Microelectronic Design Excellent Centre, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haidi","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Syed Salim","family":"Syed Ali","sequence":"additional","affiliation":[{"name":"Felda Agricultural Services Sdn Bhd, Pusat Perkhidmatan Pertanian Tun Razak, Beg Berkunci No. 3, 26400 Bandar Jengka, Pahang Darul Makmur, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2012,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0260-8774(03)00183-3","article-title":"Improving quality inspection of food products by computer vision\u2014A review","volume":"61","author":"Brosnan","year":"2004","journal-title":"J. Food Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Corley, R.H.V., and Tinker, P.B. (2003). The Oil Palm, Blackwell Science Ltd.","DOI":"10.1002\/9780470750971"},{"key":"ref_3","unstructured":"Vaughan, J., Nicholson, B., Geissler, C., Dowle, E., and Rice, E. (2009). The New Oxford Book of Food Plants, Oxford University Press."},{"key":"ref_4","first-page":"653","article-title":"Mechanization in Oil Palm Plantations","volume":"1","author":"Basiron","year":"2000","journal-title":"Advances in Oil Palm Research"},{"key":"ref_5","unstructured":"Jalil, A. (1994, January 7\u20138). Grading of FFB for Palm Oil Mills in Malaysia. Kuala Lumpur, Malaysia."},{"key":"ref_6","unstructured":"Malaysian Palm Oil Board (2003). Oil Palm Fruit Grading Manual, Malaysian Palm Oil Board."},{"key":"ref_7","first-page":"38","article-title":"Optical properties for mechanical harvesting of oil palm FFB","volume":"12","author":"Ismail","year":"2000","journal-title":"J. Oil Palm Res."},{"key":"ref_8","unstructured":"Ghazali, K.H., Samad, R., Arshad, N.W., and Karim, R.A. (2009, January 20). Image Processing Analysis of Oil Palm Fruits for Automatic Grading. Bandung, Indonesia."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2202\/1556-3758.1090","article-title":"Digital image processing of palm oil fruits","volume":"2","author":"Choong","year":"2006","journal-title":"Int. J. Food Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.3923\/jas.2008.1444.1452","article-title":"Oil palm fruit bunch grading system using red, green and blue digital number","volume":"8","author":"Alfatni","year":"2008","journal-title":"J. Appl. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"244","DOI":"10.3923\/jse.2010.244.256","article-title":"Parameter acceptance of software development for oil palm fruit maturity prediction","volume":"4","author":"Hudzari","year":"2010","journal-title":"J. Softw. Eng."},{"key":"ref_12","first-page":"18","article-title":"Photogrammetric grading of oil palm fresh fruit bunches","volume":"9","author":"Jaffar","year":"2009","journal-title":"Int. J. Mech. Mechatron. Eng."},{"key":"ref_13","unstructured":"Guan, L.C. (2005). Stepwise Discriminant Analysis on Oil Palm Fruit's Hues for Ripeness Grading Using Machine Vision System. [M.Sc. Thesis, School of Electrical and Electronic Engineering, Universiti Sains Malaysia]."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1002\/ejlt.201000020","article-title":"Imaging technique for quantification of oil palm fruit ripeness and oil content","volume":"112","author":"Tan","year":"2010","journal-title":"Eur. J. Lipid Sci. Technol."},{"key":"ref_15","unstructured":"Ismail, W.I.W., and Razali, M.H. Hue Optical Properties to Model Oil Palm Fresh Fruit Bunches Maturity Index. Orlando, FL, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7799","DOI":"10.3390\/s110807799","article-title":"A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration","volume":"11","author":"Zakaria","year":"2011","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4721","DOI":"10.3390\/s110504721","article-title":"Crop classification by forward neural network with adaptive chaotic particle swarm optimization","volume":"11","author":"Zhang","year":"2011","journal-title":"Sensors"},{"key":"ref_18","first-page":"72","article-title":"Potential application of color and hyperspectral images for estimation of weight and ripeness of oil palm (Elaeis guineensis Jacq. var. tenera)","volume":"18","author":"Junkwon","year":"2009","journal-title":"Agric. Inf. Res."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jamil, N., Mohamed, A., and Abdullah, S. (2009, January 31). Automated grading of palm oil Fresh Fruit Bunches (FFB) using neuro-fuzzy technique. Malacca, Malaysia.","DOI":"10.1109\/SoCPaR.2009.57"},{"key":"ref_20","first-page":"30","article-title":"Automated oil palm fruit grading system using artificial intelligence","volume":"11","author":"May","year":"2011","journal-title":"Int. J. Eng. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1007\/978-0-387-75181-8_21","article-title":"Clustering and classification algorithms in food and agricultural applications: A survey","volume":"25","author":"Chinchuluun","year":"2009","journal-title":"Advances in Modeling Agricultural Systems"},{"key":"ref_22","unstructured":"Gonzalez, R., and Woods, R. (2010). Digital Image Processing, Prentice-Hall. [3rd ed.]."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1111\/j.1745-4549.2002.tb00481.x","article-title":"Color vision system for ripeness inspection of oil palm elaeis guineensis","volume":"26","author":"Abdullah","year":"2002","journal-title":"J. Food Process. Pres."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.3390\/s110201721","article-title":"Leg motion classification with artificial neural networks using wavelet-based features of gyroscope signals","volume":"11","author":"Barshan","year":"2011","journal-title":"Sensors"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1468","DOI":"10.3390\/s120201468","article-title":"Performance study of the application of artificial neural networks to the completion and prediction of data retrieved by underwater sensors","volume":"12","author":"Aguiar","year":"2012","journal-title":"Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6270","DOI":"10.3390\/s110606270","article-title":"Robust crop and weed segmentation under uncontrolled outdoor illumination","volume":"11","author":"Jeon","year":"2011","journal-title":"Sensors"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1016\/S0045-7949(01)00039-6","article-title":"Neural network design for engineering applications","volume":"79","author":"Rafiq","year":"2001","journal-title":"Comput. Struct."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/j.jfoodeng.2005.05.053","article-title":"Discrimination and classification of fresh-cut starfruits (Averrhoa carambola L.) using automated machine vision system","volume":"76","author":"Abdullah","year":"2006","journal-title":"J. Food Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Priddy, K.L., and Keller, P.E. (2005). Artificial Neural Networks: An Introduction, SPIE Publications.","DOI":"10.1117\/3.633187"},{"key":"ref_30","unstructured":"Jollife, I. (2002). Principal Component Analysis, Springer."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/12\/10\/14179\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:53:01Z","timestamp":1760219581000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/12\/10\/14179"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,10,22]]},"references-count":30,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2012,10]]}},"alternative-id":["s121014179"],"URL":"https:\/\/doi.org\/10.3390\/s121014179","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,10,22]]}}}