Machine Learning Technique for Precision Agriculture Applications in 5G-Based Internet of Things
Monitoring systems based on artificial intelligence (AI) and wireless sensors are in high demand and give exact data extraction and analysis. The main objective of this paper is to detect the most appropriate plant development parameters. This paper has the concept of reducing the hazards in agricul...
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creator | Murugamani, C. Shitharth, S. Hemalatha, S. Kshirsagar, Pravin R. Riyazuddin, K. Naveed, Quadri Noorulhasan Islam, Saiful Mazher Ali, Syed Parween Batu, Areda |
description | Monitoring systems based on artificial intelligence (AI) and wireless sensors are in high demand and give exact data extraction and analysis. The main objective of this paper is to detect the most appropriate plant development parameters. This paper has the concept of reducing the hazards in agriculture and promoting intelligent farming. Advancement in agriculture is not new, but the AI-based wireless sensor will push intelligent agriculture to a new standard. The research goal of this work is to improve the prediction state using image processing-based machine learning techniques. The main objective of the paper, as described above, is to detect and control cotton leaf diseases. This paper comprises several aspects, including leaf disease detection, remote monitoring system depending on the server, moisture and temperature sensing, and soil sensing. Insects and pathogens are typically responsible for plant diseases that reduce productivity if not timely. This paper presents a method to monitor the soil quality and prevent cotton leaf diseases. The proposed system suggested uses a regression technique of artificial intelligence to identify and classify leaf diseases. The information would be delivered to farmers through the Android app after infection identification. The Android app also allows soil parameter values like moisture, humidity, and temperature to be displayed along with the chemical level in a container. The relay may be on/off to regulate the motor and chemical sprinkler system as required by using the Android app. In the proposed system, the SVM algorithm delivers the best accuracy in detecting various diseases and demonstrates its efficiency in the detection and control by the improvement of cultivation for the farmers. |
doi_str_mv | 10.1155/2022/6534238 |
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The main objective of this paper is to detect the most appropriate plant development parameters. This paper has the concept of reducing the hazards in agriculture and promoting intelligent farming. Advancement in agriculture is not new, but the AI-based wireless sensor will push intelligent agriculture to a new standard. The research goal of this work is to improve the prediction state using image processing-based machine learning techniques. The main objective of the paper, as described above, is to detect and control cotton leaf diseases. This paper comprises several aspects, including leaf disease detection, remote monitoring system depending on the server, moisture and temperature sensing, and soil sensing. Insects and pathogens are typically responsible for plant diseases that reduce productivity if not timely. This paper presents a method to monitor the soil quality and prevent cotton leaf diseases. The proposed system suggested uses a regression technique of artificial intelligence to identify and classify leaf diseases. The information would be delivered to farmers through the Android app after infection identification. The Android app also allows soil parameter values like moisture, humidity, and temperature to be displayed along with the chemical level in a container. The relay may be on/off to regulate the motor and chemical sprinkler system as required by using the Android app. In the proposed system, the SVM algorithm delivers the best accuracy in detecting various diseases and demonstrates its efficiency in the detection and control by the improvement of cultivation for the farmers.</description><identifier>ISSN: 1530-8669</identifier><identifier>EISSN: 1530-8677</identifier><identifier>DOI: 10.1155/2022/6534238</identifier><language>eng</language><publisher>Oxford: Hindawi</publisher><subject>Agricultural production ; Agriculture ; Algorithms ; Artificial intelligence ; Cotton ; Image processing ; Insects ; Internet of Things ; Machine learning ; Moisture effects ; Parameter identification ; Plant diseases ; Remote monitoring ; Remote sensing ; Sensors ; Soil moisture ; Soils ; Sprinkler systems ; Support vector machines</subject><ispartof>Wireless communications and mobile computing, 2022-06, Vol.2022, p.1-11</ispartof><rights>Copyright © 2022 C. Murugamani et al.</rights><rights>Copyright © 2022 C. Murugamani et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c337t-ed4e6822678b0b3aca55fb795e690052312b8a4e3fe4a8d9e4a809f983ae8c683</citedby><cites>FETCH-LOGICAL-c337t-ed4e6822678b0b3aca55fb795e690052312b8a4e3fe4a8d9e4a809f983ae8c683</cites><orcidid>0000-0001-7556-2022 ; 0000-0002-0972-238X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><contributor>Hashmi, Mohammad Farukh</contributor><creatorcontrib>Murugamani, C.</creatorcontrib><creatorcontrib>Shitharth, S.</creatorcontrib><creatorcontrib>Hemalatha, S.</creatorcontrib><creatorcontrib>Kshirsagar, Pravin R.</creatorcontrib><creatorcontrib>Riyazuddin, K.</creatorcontrib><creatorcontrib>Naveed, Quadri Noorulhasan</creatorcontrib><creatorcontrib>Islam, Saiful</creatorcontrib><creatorcontrib>Mazher Ali, Syed Parween</creatorcontrib><creatorcontrib>Batu, Areda</creatorcontrib><title>Machine Learning Technique for Precision Agriculture Applications in 5G-Based Internet of Things</title><title>Wireless communications and mobile computing</title><description>Monitoring systems based on artificial intelligence (AI) and wireless sensors are in high demand and give exact data extraction and analysis. 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The proposed system suggested uses a regression technique of artificial intelligence to identify and classify leaf diseases. The information would be delivered to farmers through the Android app after infection identification. The Android app also allows soil parameter values like moisture, humidity, and temperature to be displayed along with the chemical level in a container. The relay may be on/off to regulate the motor and chemical sprinkler system as required by using the Android app. In the proposed system, the SVM algorithm delivers the best accuracy in detecting various diseases and demonstrates its efficiency in the detection and control by the improvement of cultivation for the farmers.</abstract><cop>Oxford</cop><pub>Hindawi</pub><doi>10.1155/2022/6534238</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-7556-2022</orcidid><orcidid>https://orcid.org/0000-0002-0972-238X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Agricultural production Agriculture Algorithms Artificial intelligence Cotton Image processing Insects Internet of Things Machine learning Moisture effects Parameter identification Plant diseases Remote monitoring Remote sensing Sensors Soil moisture Soils Sprinkler systems Support vector machines |
title | Machine Learning Technique for Precision Agriculture Applications in 5G-Based Internet of Things |
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