An Integrated (Crop Model, Cloud and Big Data Analytic) Framework to support Agriculture Activity Monitoring System

TL;DR

This paper proposes an integrated framework combining crop modeling, cloud computing, and big data analytics to enhance agriculture activity monitoring, enabling scalable, real-time, and accessible data management from diverse sources and devices.

Contribution

It introduces a novel geo-information framework that integrates crop models with cloud and big data technologies for improved agricultural monitoring.

Findings

Supports real-time data access and collaboration.

Ensures scalability and transparency across locations.

Facilitates unstructured to structured data processing.

Abstract

Agriculture activity monitoring needs to deal with large amounts of data originating from various organizations (weather stations, agriculture repositories, field management, farm management, universities, etc.) and mass people. Therefore, a scalable environment with flexible information access, easy communication, and real-time collaboration from all types of computing devices, including mobile handheld devices such as smartphones, PDAs and iPads, Geo-sensor devices, etc. are essential. The system must be accessible, scalable, and transparent from location, migration, and resources. In addition, the framework should support modern information retrieval and management systems, unstructured information to structured information processing, task prioritization, task distribution, workflow and task scheduling systems, processing power, and data storage. Thus, High Scalability Computing (HSC) or Cloud-based systems with Big data analytics can be a prominent and convincing solution for this circumstance. In this paper, we are going to propose an integrated (crop model, cloud, and big data analytics) geo-information framework to support agriculture activity monitoring systems.