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Author

T. Sindhu

Bio: T. Sindhu is an academic researcher from Anna University. The author has contributed to research in topics: Precision agriculture & Cloud storage. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Papers
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Proceedings ArticleDOI
R. Reshma, V. Sathiyavathi, T. Sindhu1, K. Selvakumar, L. SaiRamesh1 
07 Oct 2020
TL;DR: The proposed IoT system is composed of pH sensors, Humidity and temperature sensors, Soil moisture sensors, soil nutrient sensors (NPK) probes, microcontroller/microprocessor equipped with WiFi and Cloud storage, which helps to enhance the growth using an optimized farming process.
Abstract: Agriculture aided by IoT is called Smart Agriculture and it gives rise to precision farming. Soil Monitoring combined with Internet of Things (IoT) technology aids in the enhancement of agriculture by increasing yield through gauging the exact soil characteristics such as Moisture, Temperature, Humidity, PH, and Nutrition content/Fertility. This data is then gathered in cloud storage and with the appropriate data operations; it enabled us to optimize farming strategies and helped create a trend analysis. This, then, allows us to precisely utilize resources and steer the farming methods in prudent ways to optimize yield. The proposed IoT system is composed of pH sensors, Humidity and temperature sensors, Soil moisture sensors, soil nutrient sensors (NPK) probes, microcontroller/microprocessor equipped with WiFi and Cloud storage. When the sensors are implemented, they measure the corresponding characteristics and transmit time-stamped live data to the cloud server. These sensors work together and provide wholesome data to the analyst. For the recommending system, the SVM and Decision Tree algorithm is proposed to get the crop suitable for the given soil data and helps to enhance the growth using an optimized farming process.

16 citations


Cited by
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Journal ArticleDOI
01 Mar 2021
TL;DR: This paper provides a holistic coverage of the Internet of Things in Smart Cities by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoTbased Smart City systems.
Abstract: Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

153 citations

Journal ArticleDOI
TL;DR: In this article , the authors collected the current trends in AI studies for Smart Farming papers using the latest year features from 2018-2022, and used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) of 534 articles.
Abstract: Current technology has been widely applied for development, one of which has an Artificial Intelligence (AI) applied to Smart Farming. AI can give special capabilities to be programmed as needed. In cooperation with agricultural systems, AI is part of improving the quality of agriculture. This technology is no stranger to being applied in basic fields such as agriculture. This smart technology is needed to increase crop yields for various regions by utilizing the current trends paper. This is necessary because less land is available for agriculture, and there is a greater need for food sources. Therefore, this systematic review aims to collect the current trends in AI studies for Smart Farming papers using the latest year features from 2018-2022. This paper is handy for researchers and industry in looking for the latest papers on research to enhance crop yields. The authors utilized Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) of 534 articles from IEEE, ACM, MDPI, IAES, and ScienceDirect. After going through a careful process, 67 papers were found that were judged according to the criteria. After the authors got some of the current trends, the author has discussed several factors regarding the results obtained to enhance crop yields, such as Weather, Soil, Irrigation, Unmanned Aerial Vehicle (UAV), Pest Control, Weed Control, and Disease Control.

9 citations

Journal ArticleDOI
TL;DR: In this article , the authors differentiate between traditional agriculture and smart agriculture based on the deployment architectures along with a focus on the various processing stages in smart agriculture, and present a comprehensive review of various types of sensors that are playing a vital role in enabling smart agriculture.
Abstract: Smart agriculture integrates key information communication technologies with sensing technologies to provide effective and cost-efficient agricultural services. Smart agriculture leverages a wide range of advanced technologies, such as wireless sensor networks, Internet of Things, robotics, agricultural bots, drones, artificial intelligence, and cloud computing. The adoption of these technologies in smart agriculture enables all stakeholders in the agricultural sector to develop better managerial decisions to get more yield. We differentiate between traditional agriculture and smart agriculture based on the deployment architectures along with a focus on the various processing stages in smart agriculture. We present a comprehensive review of various types of sensors that are playing a vital role in enabling smart agriculture. We also review the integration of various sensing technologies with emerging technologies and computing infrastructures to make agriculture smarter. Finally, we discuss open research challenges that must be addressed to improve the adoption and deployment of smart agriculture in the future.

8 citations

Journal ArticleDOI
TL;DR: In this article , the authors differentiate between traditional agriculture and smart agriculture based on the deployment architectures along with a focus on the various processing stages in smart agriculture, and present a comprehensive review of various types of sensors that are playing a vital role in enabling smart agriculture.
Abstract: Smart agriculture integrates key information communication technologies with sensing technologies to provide effective and cost-efficient agricultural services. Smart agriculture leverages a wide range of advanced technologies, such as wireless sensor networks, Internet of Things, robotics, agricultural bots, drones, artificial intelligence, and cloud computing. The adoption of these technologies in smart agriculture enables all stakeholders in the agricultural sector to develop better managerial decisions to get more yield. We differentiate between traditional agriculture and smart agriculture based on the deployment architectures along with a focus on the various processing stages in smart agriculture. We present a comprehensive review of various types of sensors that are playing a vital role in enabling smart agriculture. We also review the integration of various sensing technologies with emerging technologies and computing infrastructures to make agriculture smarter. Finally, we discuss open research challenges that must be addressed to improve the adoption and deployment of smart agriculture in the future.

5 citations

Journal ArticleDOI
TL;DR: In proposed work, the correlation between the labels is finding by using the Fire Fly Algorithm (FFA) and the experimental results are evaluated with the 20newsgroups dataset with better performance.
Abstract: Nowadays multi-label data are numerously available in real-world applications. Multi-label data instances are associated with more number of class labels at same time. Generally, the multi-label classification is done in many ways. Recognize of label correlation in multi-label data is difficult. In proposed work, the correlation between the labels is finding by using the Fire Fly Algorithm (FFA). The experimental results are evaluated with the 20newsgroups dataset with better performance.

3 citations