Global Journal of Science Frontier Research, A: Physics and Space Science, Volume 23 Issue 1
Figure 3: Hadoop Analytics tools for Big Data analysis IV. C loud- B ased A rchitecture Open-source tools can be used for Knowledge Agriculture data analytics, visualization, and undertaking corrective action using BI (Business Intelligence) tools like a tableau. However, this needs hardware and software infrastructure with competent programmers. The alternate solution is to use Cloud-Based platform which offers “SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service)” (IBM Cloud Education, n. a.) . SaaS utilizes the internet to deliver applications that run directly through web browsers, thus sparing the employees and companies from the efforts of installing, managing, and upgrading software. PaaS provides a framework for developers to build upon and create customized applications. IaaS is a form of virtual cloud computing technology that provides highly scalable infrastructure, including servers, networks, operating systems, and storage. Using IaaS, businesses hire resources on- demand and scale it as per their requirements instead of having to buy the hardware outright. Google Cloud Platform Services (GCP), Amazon Web Services (AWS), Microsoft Azure, Digital Ocean, Linode, Rackspace, and Cisco Metapod are a few companies providing IaaS. In 2020, the cloud-based segment had the largest share of the agriculture analytics market. The large share of cloud-based market is primarily due to easy accessibility, lower maintenance, affordable pricing, and reliable security. Furthermore, benefits provided by cloud-based solutions in terms of SaaS, PaaS, and IaaS for agriculture analytics are helping the segment grow at the highest speed. A few basic architectures based on a few major cloud service providers for big farm data analytics are discussed below. a) Google Cloud Platform (GCP)-based architecture A typical system architecture based on the Google cloud platform is shown in Figure 4. Here, data from IOT devices are captured using MQTT protocol through IOT core (fully managed Service for securely connecting and managing millions of IoT devices), and data is prepared through DataPrep (explores, cleans, and prepares structured and unstructured data for analysis). Thereafter this datais analyzed along with the other historical database stored in Firestore (a NoSQL document database), using Cloud Functions (a serverless execution environment for building and connecting cloud services) and BigQuery/ML that creates and executes machine learning models. The information after analysis is made available to Data analysts/farmers through Pub/Sub, which provides messaging between applications. 1 Year 2023 2 © 2023 Global Journals Global Journal of Science Frontier Research Volume XXIII Issue ersion I VI ( A ) Data-Driven Knowledge Agriculture: A Paradigm Shift for Enhancing Farm Productivity & Global Food Security
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