{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:50:52Z","timestamp":1775145052969,"version":"3.50.1"},"reference-count":39,"publisher":"Informa UK Limited","issue":"7","content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Applied Artificial Intelligence"],"published-print":{"date-parts":[[2019,6,7]]},"DOI":"10.1080\/08839514.2019.1592343","type":"journal-article","created":{"date-parts":[[2019,4,5]],"date-time":"2019-04-05T17:41:40Z","timestamp":1554486100000},"page":"621-642","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":136,"title":["Performance Evaluation of Best Feature Subsets for Crop Yield Prediction Using Machine Learning Algorithms"],"prefix":"10.1080","volume":"33","author":[{"given":"Maya Gopal","family":"P. S.","sequence":"first","affiliation":[{"name":"School of Computing Science and Engineering, VIT University, Chennai, India"}]},{"given":"Bhargavi","family":"R.","sequence":"additional","affiliation":[{"name":"School of Computing Science and Engineering, VIT University, Chennai, India"}]}],"member":"301","published-online":{"date-parts":[[2019,4,5]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.03.002"},{"issue":"6","key":"e_1_3_2_3_1","first-page":"402","article-title":"Determining the most important features contributing to wheat grain yield using supervised feature selection model","volume":"4","author":"Bijanzadeh E.","year":"2010","unstructured":"Bijanzadeh, E., Y. Emam, and E. Ebrahimie. 2010. Determining the most important features contributing to wheat grain yield using supervised feature selection model. Australian Journal of Crop Science 4 (6):402\u201307.","journal-title":"Australian Journal of Crop Science"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1009213726"},{"key":"e_1_3_2_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.agrformet.2018.06.001"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.05.012"},{"key":"e_1_3_2_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.08.007"},{"key":"e_1_3_2_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.geodrs.2017.07.005"},{"key":"e_1_3_2_9_1","doi-asserted-by":"publisher","DOI":"10.13031\/2013.12541"},{"key":"e_1_3_2_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11119-011-9233-6"},{"key":"e_1_3_2_11_1","first-page":"2","article-title":"Predicting wheat production in iran using an artificial neural networks approach","volume":"2","author":"Ghodsi R.","year":"2012","unstructured":"Ghodsi, R., R. Mirabdollah Yani, R. Jalali, and M. Ruzbahman. 2012. Predicting wheat production in iran using an artificial neural networks approach. International Journal of Academic Research in Business and Social Sciences 2:2. February 2012.","journal-title":"International Journal of Academic Research in Business and Social Sciences"},{"key":"e_1_3_2_12_1","doi-asserted-by":"publisher","DOI":"10.5424\/sjar\/2014122-4439"},{"key":"e_1_3_2_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.geoderma.2006.12.004"},{"key":"e_1_3_2_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2016.10.005"},{"key":"e_1_3_2_15_1","volume-title":"Data mining: Concepts and techniques","author":"Han J.","year":"2006","unstructured":"Han, J., and M. Kamber. 2006. Data mining: Concepts and techniques. 2nd ed. USA: Morgan Kaufmann Publications.","edition":"2"},{"key":"e_1_3_2_16_1","volume-title":"Principles of data mining","author":"Hand D.","year":"2001","unstructured":"Hand, D., H. Mannila, and P. Smyth. 2001. Principles of data mining. London, England: MIT Press."},{"key":"e_1_3_2_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.agrformet.2004.02.006"},{"key":"e_1_3_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/5254.708428"},{"key":"e_1_3_2_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.agsy.2018.03.001"},{"key":"e_1_3_2_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2009.08.010"},{"key":"e_1_3_2_21_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0021859606006691"},{"key":"e_1_3_2_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.agrformet.2015.11.003"},{"key":"e_1_3_2_23_1","doi-asserted-by":"crossref","first-page":"705","DOI":"10.13031\/2013.6097","article-title":"A neural network for setting target corn yields","volume":"44","author":"Liu J.","year":"2001","unstructured":"Liu, J., C. E. Goering, and L. Tian. 2001. A neural network for setting target corn yields. Transactions of the ASAE 44:705\u201313.","journal-title":"Transactions of the ASAE"},{"key":"e_1_3_2_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.agrformet.2018.01.031"},{"key":"e_1_3_2_25_1","first-page":"1554","volume-title":"International Conference of Agricultural Engineering - CIGR-AgEng 2012: Agriculture and Engineering for a Healthier Life","author":"Mehnatkesh A.","year":"2012","unstructured":"Mehnatkesh, A., A. Sh., A. Jalalian, and A. A. Dehghani. Prediction of rainfed wheat Grain yield and biomass using artificial neural networks and multiple linear regressions and determination the most factors by sensitivity analysis, information technology, automation and precision farming. In International Conference of Agricultural Engineering - CIGR-AgEng 2012: Agriculture and Engineering for a Healthier Life. CIGR-EurAgEng Valencia, Spain. 2012 8-12 July 1554. ref.9."},{"issue":"5","key":"e_1_3_2_26_1","first-page":"79","article-title":"A model for the estimation of yield and investigation on factors affecting irrigated wheat production in various tillage methods (using artificial neural networks)","volume":"3","author":"Mobarake S. A.","year":"2014","unstructured":"Mobarake, S. A., M. Almassi, A. Hemmat, and M. Z. RezaMoghaddasi. 2014. A model for the estimation of yield and investigation on factors affecting irrigated wheat production in various tillage methods (using artificial neural networks). Bulletin of Environment, Pharmacology and Life Sciences 3 (5):79\u201384.","journal-title":"Bulletin of Environment, Pharmacology and Life Sciences"},{"key":"e_1_3_2_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.agsy.2004.07.009"},{"key":"e_1_3_2_28_1","doi-asserted-by":"publisher","DOI":"10.3390\/info9010005"},{"key":"e_1_3_2_29_1","doi-asserted-by":"publisher","DOI":"10.7848\/ksgpc.2016.34.4.383"},{"key":"e_1_3_2_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2016.12.023"},{"key":"e_1_3_2_31_1","doi-asserted-by":"crossref","unstructured":"Ru\u00df G. (2009). Data mining of agricultural yield data: A comparison of regression models. Proc. 9th Indust. Conf. on Advances in Data Mining-Applications and Theoretical Aspects July 20-22 Leipzig Germany.","DOI":"10.1007\/978-3-642-03067-3_3"},{"key":"e_1_3_2_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.fcr.2016.04.028"},{"key":"e_1_3_2_33_1","doi-asserted-by":"crossref","unstructured":"Shakil Ahamed A. T. M. N. T. Mahmood N. Hossain M. T. Kabir K. Das F. Rahman and R. M. Rahman Applying data mining techniques to predict annual yield of major crops and recommend planting different crops in different districts in Bangladesh IEEE SNPD 2015 June 1-3 2015 Takamatsu Japan.","DOI":"10.1109\/SNPD.2015.7176185"},{"key":"e_1_3_2_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-1694(96)03330-6"},{"key":"e_1_3_2_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.03.023"},{"key":"e_1_3_2_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.agsy.2017.12.007"},{"key":"e_1_3_2_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2095-3119(16)61546-0"},{"key":"e_1_3_2_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2016.11.183"},{"key":"e_1_3_2_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sjbs.2017.01.024"},{"key":"e_1_3_2_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2013.05.001"}],"container-title":["Applied Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/08839514.2019.1592343","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T12:39:09Z","timestamp":1698669549000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/08839514.2019.1592343"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,5]]},"references-count":39,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2019,6,7]]}},"alternative-id":["10.1080\/08839514.2019.1592343"],"URL":"https:\/\/doi.org\/10.1080\/08839514.2019.1592343","relation":{},"ISSN":["0883-9514","1087-6545"],"issn-type":[{"value":"0883-9514","type":"print"},{"value":"1087-6545","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,5]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=uaai20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=uaai20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2019-04-05","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}