research-article

A Smart IoT-Based Irrigation System with Automated Plant Recognition using Deep Learning

Authors:

Yu SunAuthors Info & Claims

ICCMS '18: Proceedings of the 10th International Conference on Computer Modeling and Simulation

Pages 87 - 91

https://doi.org/10.1145/3177457.3177506

Published: 08 January 2018 Publication History

Abstract

Machine Learning allows systems to learn and improve automatically from experiences without hand-coding. Thus, in recent years, many technology companies have been developing such application if Artificial Intelligence, from face recognition by Facebook, to the AlphaGo program by Google. The irrigation systems in the market nowadays mostly allow users to set them to a certain amount of water and at specific time intervals. However, there are usually more than one type of plants in a garden, and each species requires different amount of water. In order to resolve this issue, in this paper, we have developed an irrigation system, with the use of deep learning, that is able to adjust the amounts of water foe each type pf plant through plants recognition. There are two main parts of the solution, the software and the hardware. The prior is connected with cameras to undergo plant recognition, and utilizes database to find the suitable amount of water; the latter controls the amount of water that is able to flow out.

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Cited By

Meddaoui MErritali M(2024)Smart Irrigation Integrate To IOT Technology Based On Deep Learning’s Algorithm LSTM2024 Mediterranean Smart Cities Conference (MSCC)10.1109/MSCC62288.2024.10697087(1-5)Online publication date: 2-May-2024
https://doi.org/10.1109/MSCC62288.2024.10697087
Kala KNandhini MKishore Chakkravarthi MThangadarshini MMadhusudhana Verma S(2024)CNN Based Pest Detection and Recommender System for Plantain TreesProceedings of International Conference on Intelligent Vision and Computing (ICIVC 2023)10.1007/978-3-031-71391-0_5(61-71)Online publication date: 30-Dec-2024
https://doi.org/10.1007/978-3-031-71391-0_5
Paria ADas R(2024)Implementation of IoT and Machine Learning Techniques in Smart Irrigation SystemsIoT Sensors, ML, AI and XAI: Empowering A Smarter World10.1007/978-3-031-68602-3_8(143-151)Online publication date: 25-Oct-2024
https://doi.org/10.1007/978-3-031-68602-3_8
Show More Cited By

Index Terms

A Smart IoT-Based Irrigation System with Automated Plant Recognition using Deep Learning
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees
2. Information systems
  1. Information systems applications
    1. Mobile information processing systems

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ICCMS '18: Proceedings of the 10th International Conference on Computer Modeling and Simulation

January 2018

310 pages

ISBN:9781450363396

DOI:10.1145/3177457

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Canberra: University of Canberra
University of Technology Sydney

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 January 2018

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ICCMS 2018

ICCMS 2018: The 10th International Conference on Computer Modeling and Simulation

January 8 - 10, 2018

Sydney, Australia

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Cited By

Meddaoui MErritali M(2024)Smart Irrigation Integrate To IOT Technology Based On Deep Learning’s Algorithm LSTM2024 Mediterranean Smart Cities Conference (MSCC)10.1109/MSCC62288.2024.10697087(1-5)Online publication date: 2-May-2024
https://doi.org/10.1109/MSCC62288.2024.10697087
Kala KNandhini MKishore Chakkravarthi MThangadarshini MMadhusudhana Verma S(2024)CNN Based Pest Detection and Recommender System for Plantain TreesProceedings of International Conference on Intelligent Vision and Computing (ICIVC 2023)10.1007/978-3-031-71391-0_5(61-71)Online publication date: 30-Dec-2024
https://doi.org/10.1007/978-3-031-71391-0_5
Paria ADas R(2024)Implementation of IoT and Machine Learning Techniques in Smart Irrigation SystemsIoT Sensors, ML, AI and XAI: Empowering A Smarter World10.1007/978-3-031-68602-3_8(143-151)Online publication date: 25-Oct-2024
https://doi.org/10.1007/978-3-031-68602-3_8
HAMDOON WZENGİN A(2023)A NEW IOT-BASED SMART IRRIGATION MANAGEMENT SYSTEMMiddle East Journal of Science10.51477/mejs.11421369:1(42-56)Online publication date: 26-Jun-2023
https://doi.org/10.51477/mejs.1142136
Ahmed S(2023)Energy Aware Software Defined Network Model for Communication of Sensors Deployed in Precision AgricultureSensors10.3390/s2311517723:11(5177)Online publication date: 29-May-2023
https://doi.org/10.3390/s23115177
Kasiselvanathan MSekar GPrasad JLakshminarayanan SSharanya C(2023)An IoT Based Agricultural Management Approach Using Machine Learning2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA)10.1109/ICIDCA56705.2023.10099598(61-65)Online publication date: 14-Mar-2023
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Bin Karim MRahman MShahriar SIslam M(2023)A WSN (Wireless Sensor Network) Approach for Controlling the Actuator Wirelessly through Database Interaction2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)10.1109/I-SMAC58438.2023.10290380(314-319)Online publication date: 11-Oct-2023
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Sapaev JArifjanov ARamazonov KSapaev I(2023)Smart technologies for determining water flow in irrigation systemsE3S Web of Conferences10.1051/e3sconf/202338302012383(02012)Online publication date: 24-Apr-2023
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Premkumar SSigappi A(2022)IoT-enabled edge computing model for smart irrigation systemJournal of Intelligent Systems10.1515/jisys-2022-004631:1(632-650)Online publication date: 27-May-2022
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Prasath SGovindaraj RSubbiah RAlharbi SAlmoallim HPriya SMulugeta B(2022)Development of Microbiology Plantation-Based Multimodal Segmentation for Smart Garden Using Machine LearningAdvances in Materials Science and Engineering10.1155/2022/10665352022(1-6)Online publication date: 5-Oct-2022
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