Regression Analysis of Rice Data for Yield Prediction Using Python Programming Language

I. A. Supro, J. Mahar, A. Maitlo
{"title":"Regression Analysis of Rice Data for Yield Prediction Using Python Programming Language","authors":"I. A. Supro, J. Mahar, A. Maitlo","doi":"10.26692/SUJO/2019.6.32","DOIUrl":null,"url":null,"abstract":"The interdisciplinary domain Data Science exists ubiquitously for helping to filter out status of the passive data existing over the internet through analytics techniques on Big Data. In fact, it is intricate procedure of exploring different data set to disclose facts including hidden pattern, unidentified correlations and market trend that could assist organizations make business verdicts by predicting. A number of experts are working on vegetables and fruits yield prediction, the analysis of rice yield prediction using regression analysis with Python language is presented in this paper. The rice data of District Larkana is collected from Agriculture Statistic Department, Islamabad with three factors: Area under Cultivation, Production and Yield. The linear regression technique is applied to calculate the relationship between the Area under Cultivation (Independent) and its effect on Yield (Dependent). The positive, moderate and significant relationship is observed between the dependent and independent variables. This study can helps to researchers for knowing the worth of analytics techniques for prediction of harvest.","PeriodicalId":21635,"journal":{"name":"SINDH UNIVERSITY RESEARCH JOURNAL -SCIENCE SERIES","volume":"445 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SINDH UNIVERSITY RESEARCH JOURNAL -SCIENCE SERIES","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26692/SUJO/2019.6.32","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

The interdisciplinary domain Data Science exists ubiquitously for helping to filter out status of the passive data existing over the internet through analytics techniques on Big Data. In fact, it is intricate procedure of exploring different data set to disclose facts including hidden pattern, unidentified correlations and market trend that could assist organizations make business verdicts by predicting. A number of experts are working on vegetables and fruits yield prediction, the analysis of rice yield prediction using regression analysis with Python language is presented in this paper. The rice data of District Larkana is collected from Agriculture Statistic Department, Islamabad with three factors: Area under Cultivation, Production and Yield. The linear regression technique is applied to calculate the relationship between the Area under Cultivation (Independent) and its effect on Yield (Dependent). The positive, moderate and significant relationship is observed between the dependent and independent variables. This study can helps to researchers for knowing the worth of analytics techniques for prediction of harvest.
基于Python的水稻产量预测数据回归分析
跨学科领域数据科学无处不在,通过大数据分析技术帮助过滤掉互联网上存在的被动数据状态。事实上,这是一个复杂的过程,通过探索不同的数据集来揭示事实,包括隐藏的模式、未确定的相关性和市场趋势,这些事实可以帮助组织通过预测做出商业判断。目前已有多名专家从事蔬菜和水果产量预测的研究,本文介绍了用Python语言对水稻产量预测进行回归分析的方法。拉卡纳地区的水稻数据由伊斯兰堡农业统计部门收集,有三个因素:种植面积、产量和产量。采用线性回归技术计算了耕地面积(独立)与其对产量的影响(依赖)之间的关系。因变量与自变量之间存在正、中、显著的关系。这项研究可以帮助研究人员了解收获预测分析技术的价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信