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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Veröffentlicht in:Wireless communications and mobile computing 2022-06, Vol.2022, p.1-11
Hauptverfasser: Murugamani, C., Shitharth, S., Hemalatha, S., Kshirsagar, Pravin R., Riyazuddin, K., Naveed, Quadri Noorulhasan, Islam, Saiful, Mazher Ali, Syed Parween, Batu, Areda
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container_issue
container_start_page 1
container_title Wireless communications and mobile computing
container_volume 2022
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. 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source Wiley Online Library Open Access; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection
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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