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

Alcardo Alex Barakabitze () and James Robert
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Alcardo Alex Barakabitze: Sokoine University of Agriculture
James Robert: Neggrow Company Ltd

A chapter in Smart and Secure Embedded and Mobile Systems, 2024, pp 1-10 from Springer

Abstract: 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.

Keywords: artificial intelligence; machine learning; IoTs; digital agriculture; food systems; smallholder farmers (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-56603-5_1

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