Optimum Lightweight AI End Device for Health Monitoring of Agriculture Vehicles

Gupta, Saurabh; Gupta, Neeraj

doi:10.1007/978-981-16-3128-3_11

Cited by 1 publication

(1 citation statement)

References 66 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In their recent publication, Gupta and Gupta (2022) proposed an optimal lightweight AI end device for agriculture vehicle health monitoring [16]. This study aims to create a lightweight AI system for vehicle health monitoring.…”

Section: Literature Surveymentioning

confidence: 99%

Smart Health Monitoring System of Agricultural Machines: Deep Learning-based Optimization with IoT and AI

Shwetabh,

Ambhaikar

2024

BIO Web Conf.

View full text Add to dashboard Cite

Implementing intelligent monitoring systems for Agricultural Machinery (AM) is hindered by the intricate and costly nature of the Internet of Things (IoT) sensor technologies. The heavy reliance on cloud and fog computing, the availability of network infrastructure, and the need for expert knowledge pose challenges in rural areas that lack network connectivity. Using edge devices, such as smartphones, which possess significant computational capabilities, is a potential solution that has not yet been fully realized in the commercial sphere. Furthermore, the increasing demand from users for economically viable and user-friendly technology serves as a driving force for transitioning away from expensive and intricate sensors towards more cost-effective alternatives. In the IoT era, there is anticipated to be a widespread network connection between a vast array of AM and service centers. Using smartphone applications has increased the potential for edge computation on smartphones to significantly aid in network traffic control. The development of an Artificial Intelligence (AI) - -based data analytic method poses a significant challenge due to the need to optimize for the limited computational capabilities of smartphones. However, the users’ demand for affordable technology renders it resistant to easy penetration. This paper uses IoT and AI to propose a Smart Health Monitoring System for Agricultural Machines with Deep Learning-based Optimization (SHMAM-DLO). This paper aims to propose a Fusion Genetic Algorithm (FGA) methodology and Artificial Neural Network (ANN) for optimization during monitoring the health of AM. The proposed approach enables cost-effective utilization of smartphone end devices by leveraging their built-in microphones instead of relying on expensive IoT sensors.

show abstract

Section: Literature Surveymentioning

confidence: 99%