Impact of IoT based Autonomous Farming Equipment on Crop Culture and Management in the Agricultural Sector

Kumar, Navin; Singh, Alka; Das, Deepak Kumar; Srivastava, Diksha; Talari, Venkata Syamala Raju; Kurukwar, Amardip Diwakar

doi:10.1109/icecaa55415.2022.9936243

Cited by 2 publications

(1 citation statement)

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“…Pooja et al study [27] presents a smart agriculture solution based on the MQTT protocol for continuous environmental data collection in the field and the development and implementation of intelligent technology for the agricultural sector. Kumar et al work [28] propose an intelligent agricultural monitoring and management system using the Self-Synchronizing Data Automation (SSDA) controller to improve the agricultural sector. Ojo et al work [29] presents a performance evaluation of an IoT solution for precision agriculture to repel ungulates and prevent damage to crop areas.…”

Section: Related Workmentioning

confidence: 99%

Smart Poultry Supported by Edge-Fog-Cloud Continuum: A Performance Evaluation Using Petri Nets

Barbosa,

Sabino,

Lima

et al. 2023

Preprint

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The Internet of Things (IoT) application in agriculture provides rural poultry farmers with automated tools and decision support systems to increase productivity and profit. The integration of Edge, Fog, and Cloud Computing technologies in this scenario enables real-time data collection, processing, and analysis. This, in turn, empowers poultry farmers to make informed decisions, enhancing production efficiency and ensuring poultry quality. However, when implementing IoT devices, the challenge arises in dealing with a large volume of generated data. Delays in data transfer between the Edge, Fog, and Cloud layers can impact the system's real-time responsiveness. Slowness in the system's responsiveness can compromise performance, especially in poultry monitoring systems, which are time-sensitive, and an error could result in losses. This work proposes a Stochastic Petri Net (SPN) model to analyze the performance of an integrated smart poultry house with Edge, Fog, and Cloud Computing. The proposed model covers the analysis of metrics such as average response time, resource utilization, discard probability, and system throughput. Furthermore, the highly flexible model allows developers and system administrators to calibrate various parameters.

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Section: Related Workmentioning

confidence: 99%