{"title":"Proposal of a fuzzy logic controller for the improvement of irrigation scheduling decision-making in greenhouse horticulture","authors":"A. Carrasquilla-Batista, A. Chacón-Rodríguez","doi":"10.1109/PRIME-LA.2017.7899172","DOIUrl":null,"url":null,"abstract":"A fuzzy logic controller is proposed for the evaluation of water drainage in order to determine if water content is easily available to crops; the lexical uncertainty given by “easily available” was quantified into a numerical value in this first approach. The result is a preliminary controller that embeds structural human knowledge about irrigation scheduling into an analytic fuzzy logic model (FLM), in order to provide agricultural scientists with quick access to a particular crop’s main production and growth related variables, and allowing for future data driven decisions on the spot. The controller was validated with real data collected from a particular crop, using remote sensors connected to Internet.","PeriodicalId":163037,"journal":{"name":"2017 1st Conference on PhD Research in Microelectronics and Electronics Latin America (PRIME-LA)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 1st Conference on PhD Research in Microelectronics and Electronics Latin America (PRIME-LA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PRIME-LA.2017.7899172","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
A fuzzy logic controller is proposed for the evaluation of water drainage in order to determine if water content is easily available to crops; the lexical uncertainty given by “easily available” was quantified into a numerical value in this first approach. The result is a preliminary controller that embeds structural human knowledge about irrigation scheduling into an analytic fuzzy logic model (FLM), in order to provide agricultural scientists with quick access to a particular crop’s main production and growth related variables, and allowing for future data driven decisions on the spot. The controller was validated with real data collected from a particular crop, using remote sensors connected to Internet.