A. Wahana, I. Taufik, Daniel Roberto Ramiraj, C. Alam, B. Subaeki
{"title":"Ant Colony Algorithm in Selection Suitable Plant for Urban Farming","authors":"A. Wahana, I. Taufik, Daniel Roberto Ramiraj, C. Alam, B. Subaeki","doi":"10.1109/ICWT50448.2020.9243663","DOIUrl":null,"url":null,"abstract":"Urban Farming is the right solution where this agricultural method is an agricultural method that can take advantage of the narrow open land for farming purposes. Choosing a suitable crop type for a city can give better results. This research by observing the temperature of 5 (five) big cities. The purpose of this study is to select suitable plants for a city according to the temperature of each city. The Ant Colony Optimization (ACO) algorithm is inspired by the observation of an ant colony. Ants are animals that live as a unit in their colony as opposed to being seen as individuals who live independently of the colony. The results of this study provide selection of plants suitable for cultivation in each city with an the level compatibility of 68 percent.","PeriodicalId":304605,"journal":{"name":"2020 6th International Conference on Wireless and Telematics (ICWT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 6th International Conference on Wireless and Telematics (ICWT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICWT50448.2020.9243663","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Urban Farming is the right solution where this agricultural method is an agricultural method that can take advantage of the narrow open land for farming purposes. Choosing a suitable crop type for a city can give better results. This research by observing the temperature of 5 (five) big cities. The purpose of this study is to select suitable plants for a city according to the temperature of each city. The Ant Colony Optimization (ACO) algorithm is inspired by the observation of an ant colony. Ants are animals that live as a unit in their colony as opposed to being seen as individuals who live independently of the colony. The results of this study provide selection of plants suitable for cultivation in each city with an the level compatibility of 68 percent.
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