IDEAS home Printed from https://ideas.repec.org/h/spr/prochp/978-3-031-56603-5_1.html
   My bibliography  Save this book chapter

A Machine Learning Internet of Agro Things (IoAT)—Adaptive Smart Cloud Farming System for Small-Scale Farmers in Tanzania

In: Smart and Secure Embedded and Mobile Systems

Author

Listed:
  • Alcardo Alex Barakabitze

    (Sokoine University of Agriculture)

  • James Robert

    (Neggrow Company Ltd)

Abstract

Recent advancements in technologies such as Machine Learning (ML) and Internet of Things (IoTs) have shown to assist farmers in finding solutions to various difficulties and maximizing the use of limited resources. This chapter presents a real-time implementation of a ML-based adaptive smart farming management system with an open IoT solution over cloud computing to increase agricultural productivity of Small-Scale Farmers (SSFs) in Tanzania. The aim is to help SSFs to analyze crop-related activities in order to optimize farm productivity. The ML-based IoT smart farming system using sensor nodes is developed to enable farmers to collect massive amounts of streaming data which offers new pathways for monitoring agricultural and food processes in Tanzania. The chapter is part of the SUA’s initial ML/IoT innovative prototype implementation of (a) an efficient IoT-based cloud computing farm management system using ML to monitor real-time crop performance and provide decision support tools for SSFs, and (b) AI solutions that can utilize farm data to derive farm decisions that might improve crop management and provide insightful information on the past practices that led to good or bad yields. This chapter provides a baseline for proposing measures that will support decision-making in terms of an AI policy and intervention strategies in the context of monitoring crop performance in the farms belonging to SSFs in Tanzania.

Suggested Citation

  • Alcardo Alex Barakabitze & James Robert, 2024. "A Machine Learning Internet of Agro Things (IoAT)—Adaptive Smart Cloud Farming System for Small-Scale Farmers in Tanzania," Progress in IS, in: Jorge Marx Gómez & Anael Elikana Sam & Devotha Godfrey Nyambo (ed.), Smart and Secure Embedded and Mobile Systems, pages 1-10, Springer.
    • Handle: RePEc:spr:prochp:978-3-031-56603-5_1
    DOI: 10.1007/978-3-031-56603-5_1
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:prochp:978-3-031-56603-5_1. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.