基于物联网无人机和机器学习传播模型的智能农业预测分析

IF 0.9 Q3 ENGINEERING, MULTIDISCIPLINARY

International Journal of Engineering Systems Modelling and SImulation Pub Date : 2023-01-01 DOI:10.1504/ijesms.2023.127399

M. Kumarasamy, Balachandra Pattanaik, Jaiprakash Narain Dwivedi, B.R. Ramji, Muruganantham Ponnusamy, V. Nagaraj

{"title":"基于物联网无人机和机器学习传播模型的智能农业预测分析","authors":"M. Kumarasamy, Balachandra Pattanaik, Jaiprakash Narain Dwivedi, B.R. Ramji, Muruganantham Ponnusamy, V. Nagaraj","doi":"10.1504/ijesms.2023.127399","DOIUrl":null,"url":null,"abstract":"Every year, unfavourable weather conditions cause many crops to fail. Every time, over 12 million dollar losses are recorded. This article provides a proper background for delivering the yield's current state. The project proposes to employ IoT-based unmanned aerial vehicles (UAVs) and tensor-flow machine learning to estimate crop yields. This framework enhances agricultural yield accuracy by using UAVs. The IoT-enabled UAV module captures data and texts it to the farmer or rancher. The data cloud storage's server uses MQTT for safe data transmission. The cloud server leverages UAV for continuous surveillance and harvest forecasts. Predictive analysis using propagation model has an accuracy of roughly 85% compared to real-time analysis for the same crops at the farm.","PeriodicalId":51938,"journal":{"name":"International Journal of Engineering Systems Modelling and SImulation","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Predictive analysis of smart agriculture using IoT-based UAV and propagation models of machine learning\",\"authors\":\"M. Kumarasamy, Balachandra Pattanaik, Jaiprakash Narain Dwivedi, B.R. Ramji, Muruganantham Ponnusamy, V. Nagaraj\",\"doi\":\"10.1504/ijesms.2023.127399\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Every year, unfavourable weather conditions cause many crops to fail. Every time, over 12 million dollar losses are recorded. This article provides a proper background for delivering the yield's current state. The project proposes to employ IoT-based unmanned aerial vehicles (UAVs) and tensor-flow machine learning to estimate crop yields. This framework enhances agricultural yield accuracy by using UAVs. The IoT-enabled UAV module captures data and texts it to the farmer or rancher. The data cloud storage's server uses MQTT for safe data transmission. The cloud server leverages UAV for continuous surveillance and harvest forecasts. Predictive analysis using propagation model has an accuracy of roughly 85% compared to real-time analysis for the same crops at the farm.\",\"PeriodicalId\":51938,\"journal\":{\"name\":\"International Journal of Engineering Systems Modelling and SImulation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Engineering Systems Modelling and SImulation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijesms.2023.127399\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Engineering Systems Modelling and SImulation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijesms.2023.127399","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 2

摘要

每年，不利的天气条件导致许多作物歉收。每次都有超过1200万美元的损失记录。本文为交付yield的当前状态提供了适当的背景知识。该项目建议使用基于物联网的无人机(uav)和张量流机器学习来估计作物产量。该框架通过使用无人机提高了农业产量的准确性。支持物联网的无人机模块捕获数据并将其发送给农民或牧场主。数据云存储的服务器使用MQTT进行安全的数据传输。云服务器利用无人机进行持续监视和收获预测。使用繁殖模型进行预测分析，与在农场对相同作物进行实时分析相比，准确率约为85%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Predictive analysis of smart agriculture using IoT-based UAV and propagation models of machine learning

Every year, unfavourable weather conditions cause many crops to fail. Every time, over 12 million dollar losses are recorded. This article provides a proper background for delivering the yield's current state. The project proposes to employ IoT-based unmanned aerial vehicles (UAVs) and tensor-flow machine learning to estimate crop yields. This framework enhances agricultural yield accuracy by using UAVs. The IoT-enabled UAV module captures data and texts it to the farmer or rancher. The data cloud storage's server uses MQTT for safe data transmission. The cloud server leverages UAV for continuous surveillance and harvest forecasts. Predictive analysis using propagation model has an accuracy of roughly 85% compared to real-time analysis for the same crops at the farm.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

International Journal of Engineering Systems Modelling and SImulation ENGINEERING, MULTIDISCIPLINARY-

CiteScore

2.00

自引率

27.30%

发文量

期刊介绍： Most of the research and experiments in the field of engineering have devoted significant efforts to modelling and simulation of various complicated phenomena and processes occurring in engineering systems. IJESMS provides an international forum and refereed authoritative source of information on the development and advances in modelling and simulation, contributing to the understanding of different complex engineering systems. IJESMS is designed to be a multi-disciplinary, fully refereed, international journal.