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Deep Learning Based IoT Module for Smart Farming in Different Environmental Conditions

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Abstract

Internet of Things (IoT) is an unavoidable technology evolution in the present era. All most all the domains have accepted IoT in their applications. Recently, IoT is adopted in agriculture as smart farming to collect environmental and crop data. IoT devices are used to collect data from sensors and it can be analyzed for further improvement of farming. This research work proposed a sensor based intelligent control system using IoT for smart agriculture measure that collects environmental data and incorporates an automatic irrigation system. The proposed system helps the farmers to increase cultivation using the data collected from IoT devices and provide adequate water supply to the crops using an automatic irrigation system. Fundamental field information like Ultraviolet range, humidity, temperature, light intensity, soil moisture is measured through IoT devices during the growing season. Users can monitor the field based on the collected information as a continuous process using specified user address. The information is transmitted for analysis and based on the analysis results smart irrigation system is developed using a fuzzy logic controller. The performance of the smart agriculture module is tested and validated against different environmental conditions to validate the effectiveness of the proposed approach.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, PSN Institute of Technology and Science, Tirunelveli, India

    R. Manikandan

  2. Department of Electronics and Communication Engineering, Gnanamani college of Technology, Namakkal, India

    G. Ranganathan

  3. Department of Electronics and Communication Engineering, PPG Institute of Technology, Coimbatore, India

    V. Bindhu

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  1. R. Manikandan
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  2. G. Ranganathan
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  3. V. Bindhu
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Correspondence to G. Ranganathan or V. Bindhu.

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Manikandan, R., Ranganathan, G. & Bindhu, V. Deep Learning Based IoT Module for Smart Farming in Different Environmental Conditions. Wireless Pers Commun 128, 1715–1732 (2023). https://doi.org/10.1007/s11277-022-10016-5

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