IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2021i1p35-d713342.html
   My bibliography  Save this article

An Agile AI and IoT-Augmented Smart Farming: A Cost-Effective Cognitive Weather Station

Author

Listed:
  • Amine Faid

    (NEST Research Group, LRI Lab, ENSEM, Hassan II University of Casablanca, Casablanca 20000, Morocco)

  • Mohamed Sadik

    (NEST Research Group, LRI Lab, ENSEM, Hassan II University of Casablanca, Casablanca 20000, Morocco)

  • Essaid Sabir

    (NEST Research Group, LRI Lab, ENSEM, Hassan II University of Casablanca, Casablanca 20000, Morocco
    Department of Computer Science, University of Quebec at Montreal (UQAM), Montreal, QC H2L 2C4, Canada)

Abstract

Internet of Things (IoT) can be seen as the electricity of 21st century. It has been reshaping human life daily during the last decade, with various applications in several critical domains such as agriculture. Smart farming is a real-world application in which Internet of Things (IoT) technologies like agro-weather stations can have a direct impact on humans by enhancing crop quality, supporting sustainable agriculture, and eventually generating steady growth. Meanwhile, most agro-weather solutions are neither customized nor affordable for small farmers within developing countries. Furthermore, due to the outdoor challenges, it is often a challenge to develop and deploy low-cost yet robust systems. Robustness, which is determined by several factors, including energy consumption, portability, interoperability, and system’s ease of use. In this paper, we present an agile AI-Powered IoT-based low-cost platform for cognitive monitoring for smart farming. The hybrid Multi-Agent and the fully containerized system continuously surveys multiple agriculture parameters such as temperature, humidity, and pressure to provide end-users with real-time environmental data and AI-based forecasts. The surveyed data is ensured through several heterogeneous nodes deployed within the base station and in the open sensing area. The collected data is transmitted to the local server for pre-processing and the cloud server for backup. The system backbone communication is based on heterogeneous protocols such as MQTT, NRF24L01, and WiFi for radio communication. We also set up a user-friendly web-based graphical user interface (GUI) to support different user profiles. The overall platform design follows an agile approach to be easy to deploy, accessible to maintain, and continuously modernized.

Suggested Citation

  • Amine Faid & Mohamed Sadik & Essaid Sabir, 2021. "An Agile AI and IoT-Augmented Smart Farming: A Cost-Effective Cognitive Weather Station," Agriculture, MDPI, vol. 12(1), pages 1-31, December.
    • Handle: RePEc:gam:jagris:v:12:y:2021:i:1:p:35-:d:713342
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/1/35/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/1/35/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aadhityaa Mohanavelu & Sujay Raghavendra Naganna & Nadhir Al-Ansari, 2021. "Irrigation Induced Salinity and Sodicity Hazards on Soil and Groundwater: An Overview of Its Causes, Impacts and Mitigation Strategies," Agriculture, MDPI, vol. 11(10), pages 1-17, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuhui Yang & Dongwei Li & Weixiong Huang & Xinguo Zhou & Zhaoyang Li & Xiaomei Dong & Xingpeng Wang, 2022. "Effects of Subsurface Drainage on Soil Salinity and Groundwater Table in Drip Irrigated Cotton Fields in Oasis Regions of Tarim Basin," Agriculture, MDPI, vol. 12(12), pages 1-14, December.
    2. Mohammed, Safwan & Arshad, Sana & Bashir, Bashar & Vad, Attila & Alsalman, Abdullah & Harsányi, Endre, 2024. "Machine learning driven forecasts of agricultural water quality from rainfall ionic characteristics in Central Europe," Agricultural Water Management, Elsevier, vol. 293(C).
    3. Hesham M. Aboelsoud & Mohamed A. E. AbdelRahman & Ahmed M. S. Kheir & Mona S. M. Eid & Khalil A. Ammar & Tamer H. Khalifa & Antonio Scopa, 2022. "Quantitative Estimation of Saline-Soil Amelioration Using Remote-Sensing Indices in Arid Land for Better Management," Land, MDPI, vol. 11(7), pages 1-19, July.
    4. Mădălina Trușcă & Ștefania Gâdea & Roxana Vidican & Vlad Stoian & Anamaria Vâtcă & Claudia Balint & Valentina Ancuța Stoian & Melinda Horvat & Sorin Vâtcă, 2023. "Exploring the Research Challenges and Perspectives in Ecophysiology of Plants Affected by Salinity Stress," Agriculture, MDPI, vol. 13(3), pages 1-19, March.
    5. Vinod Phogat & Tim Pitt & Paul Petrie & Jirka Šimůnek & Michael Cutting, 2023. "Optimization of Irrigation of Wine Grapes with Brackish Water for Managing Soil Salinization," Land, MDPI, vol. 12(10), pages 1-29, October.
    6. Birhanu Iticha & Muhammad Kamran & Rui Yan & Dorota Siuta & Abdulrahman Al-Hashimi & Chalsissa Takele & Fayisa Olana & Bożena Kukfisz & Shehzad Iqbal & Mohamed S. Elshikh, 2022. "The Role of Digital Soil Information in Assisting Precision Soil Management," Sustainability, MDPI, vol. 14(18), pages 1-13, September.
    7. Azamat Suleymanov & Ilyusya Gabbasova & Mikhail Komissarov & Ruslan Suleymanov & Timur Garipov & Iren Tuktarova & Larisa Belan, 2023. "Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas," Agriculture, MDPI, vol. 13(5), pages 1-11, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2021:i:1:p:35-:d:713342. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.