![]() ![]() It makes it challenging for farmers to choose the ideal time to plant seeds. To cultivate healthier crops, manage pests, monitor soil and growing conditions, analyse data for farmers, and enhance other management activities of the food supply chain, the agriculture sector is turning to AI technology. Machine learning will become more popular in the coming years as IoT networks take center stage in a variety of industries.Īrtificial Intelligence (AI) has been extensively applied in farming recently. In the future, IoT will continue to serve as the foundation for many technologies. This elegant combination provides excellent value in automatic action. This then cascades into the four stages of the IoT architectural layout: sensors and actuators gateways and data acquisition systems edge IT data processing and datacenter and cloud, which use high-end apps to collect data, evaluate it, process it, and provide remedial solutions. The IoT system architecture is divided into three layers: device, gateway, and platform. This paper explains how an appropriate IoT architecture that saves data, analyzes it, and recommends corrective action improves the process’s ground reality. This paper discusses the current state of the IoT ecosystem, its primary applications and benefits, important architectural stages, some of the problems and challenges it faces, and its future. Consumer markets, wearable devices, healthcare, smart buildings, agriculture, and smart cities are just a few examples. The internet of things (IoT) is rapidly expanding and improving operations in a wide range of real-world applications, from consumer IoT and enterprise IoT to manufacturing and industrial IoT (IIoT). By using the improved watering system, it improved the efficacy of watering system which saves more water by 13.89% The algorithm obtained was used to write function to control the automated watering system to make sure that the temperature and humidity for growing maize is at appropriate condition. ![]() ![]() By comparing several algorithms including artificial neural network (ANN), decision tree, naïve bayes, and deep learning, it was found that deep learning algorithm can provide the most accurate result at 99.6% with root mean square error (RMSE)=0.0039. From the analysis, the optimization result showed 3 classes and these data were further analyzed through prediction to identify precision. Next, these data would undergo data cleansing in order to group them to obtain optimization clustering to identify the optimum condition and amount of water required to grow the maize through k-mean technique. Data science tools such as rapidminer studio was used for data cleansing, data imputation, clustering, and prediction. It comes with rich data mining analysis and algorithm functions, and is often used to solve a variety of business critical problems.This research focused on testing with maize, economical crop grown in Phetchabun province, Thailand, by installing a total of 20 sets of internet of things (IoT) devices which consist of soil moisture sensors and temperature and humidity sensors (DHT11). RapidMiner (originally called YALE = Yet Another Learning Environment) is a data science software platform for Windows, Mac and Linux platforms, developed by the company of the same name from Germany in 2001, written in Java. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Otherwise, please bear all the consequences by yourself. ![]() Otherwise, you may receive a variety of copyright complaints and have to deal with them by yourself.īefore using (especially downloading) any resources shared by AppNee, please first go to read our F.A.Q. page more or less. To repost or reproduce, you must add an explicit footnote along with the URL to this article!Īny manual or automated whole-website collecting/crawling behaviors are strictly prohibited.Īny resources shared on AppNee are limited to personal study and research only, any form of commercial behaviors are strictly prohibited. This article along with all titles and tags are the original content of AppNee. ![]()
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