Spatial distribution of soil nutrient content for sustainable rice agriculture using geographic information system and Naïve Bayes classifier

IF 0.5 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal on Smart Sensing and Intelligent Systems Pub Date : 2023-01-01 DOI:10.2478/ijssis-2023-0001

A. Yudhana, Andreyan Dwi Cahyo, L. Y. Sabila, Arsyad Cahya Subrata, I. Mufandi

{"title":"Spatial distribution of soil nutrient content for sustainable rice agriculture using geographic information system and Naïve Bayes classifier","authors":"A. Yudhana, Andreyan Dwi Cahyo, L. Y. Sabila, Arsyad Cahya Subrata, I. Mufandi","doi":"10.2478/ijssis-2023-0001","DOIUrl":null,"url":null,"abstract":"Abstract This study aims to assist farmers in monitoring soil nutrients, especially phosphorus. To measure the phosphorus content of paddy soil, the TCS3200 converter, as an intelligent sensor, was applied. The geographical information system (GIS) was also involved in this research to map the phosphorus content. In addition, the Naïve Bayes method was applied to classify lowland soil phosphorus status. The result of this study indicated that the Naïve Bayes algorithm could classify lowland soil phosphorus status with a probability of 0.34 for moderate phosphorus conditions and 0.66 for high phosphorus conditions. The sample testing results showed that the error rate was 3% and the success rate was 97%. Testing with a phosphorus-measuring instrument can be carried out by mapping the soil phosphorus status with the ArcGIS software, whereby seven points of medium-phosphorus-status paddy soil and 13 locations of high-phosphorus-status soil samples were determined. This research thus successfully mapped the soil phosphorus.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":" ","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal on Smart Sensing and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/ijssis-2023-0001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}

引用次数: 2

Abstract

Abstract This study aims to assist farmers in monitoring soil nutrients, especially phosphorus. To measure the phosphorus content of paddy soil, the TCS3200 converter, as an intelligent sensor, was applied. The geographical information system (GIS) was also involved in this research to map the phosphorus content. In addition, the Naïve Bayes method was applied to classify lowland soil phosphorus status. The result of this study indicated that the Naïve Bayes algorithm could classify lowland soil phosphorus status with a probability of 0.34 for moderate phosphorus conditions and 0.66 for high phosphorus conditions. The sample testing results showed that the error rate was 3% and the success rate was 97%. Testing with a phosphorus-measuring instrument can be carried out by mapping the soil phosphorus status with the ArcGIS software, whereby seven points of medium-phosphorus-status paddy soil and 13 locations of high-phosphorus-status soil samples were determined. This research thus successfully mapped the soil phosphorus.

查看原文本刊更多论文

基于地理信息系统和Naïve贝叶斯分类器的水稻可持续农业土壤养分空间分布

摘要本研究旨在帮助农民监测土壤养分，特别是磷。采用TCS3200转换器作为智能传感器，对水稻土中磷含量进行了测定。地理信息系统（GIS）也参与了这项研究，以绘制磷含量图。此外，还应用朴素贝叶斯方法对低地土壤磷状况进行了分类。研究结果表明，Naïve Bayes算法可以对低地土壤磷状况进行分类，中等磷条件下的概率为0.34，高磷条件下为0.66。样本测试结果表明，错误率为3%，成功率为97%。利用ArcGIS软件绘制土壤磷素状况图，利用测磷仪进行测试，确定了7个中等磷素状态水稻土点和13个高磷素状态土壤样品点。因此，这项研究成功地绘制了土壤磷的地图。

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

International Journal on Smart Sensing and Intelligent Systems ENGINEERING, ELECTRICAL & ELECTRONIC-

CiteScore

2.70

自引率

8.30%

发文量

审稿时长

8 weeks

期刊介绍： nternational Journal on Smart Sensing and Intelligent Systems (S2IS) is a rapid and high-quality international forum wherein academics, researchers and practitioners may publish their high-quality, original, and state-of-the-art papers describing theoretical aspects, system architectures, analysis and design techniques, and implementation experiences in intelligent sensing technologies. The journal publishes articles reporting substantive results on a wide range of smart sensing approaches applied to variety of domain problems, including but not limited to: Ambient Intelligence and Smart Environment Analysis, Evaluation, and Test of Smart Sensors Intelligent Management of Sensors Fundamentals of Smart Sensing Principles and Mechanisms Materials and its Applications for Smart Sensors Smart Sensing Applications, Hardware, Software, Systems, and Technologies Smart Sensors in Multidisciplinary Domains and Problems Smart Sensors in Science and Engineering Smart Sensors in Social Science and Humanity