DESIGNING A CLOUD ARCHITECTURE AND MOBILE APPLICATION FOR AN AGRICULTURAL PEST IDENTIFICATION SYSTEM

Abstract

The article considers approaches to designing
a cloud-based architecture for an intelligent agricultural pest identification system using a mobile
application. An analysis of modern approaches to building mobile object recognition systems is conducted
and the relevance of combining mobile technologies, cloud computing, and machine learning methods for
solving computer vision problems in conditions of limited mobile device resources is substantiated. The
system architecture based on the distribution of the computational load between the client and server parts
is proposed. The mobile application performs the functions of receiving and transmitting images, while
data processing is implemented on the server using the YOLOv11-N deep learning model. The architecture
includes an application server, a computer vision service, a mobile client, and external cloud services for
data storage and user authorization. The technological implementation of the system is described, in
particular, the use of Node.js and FastAPI for the server side, Docker containerization and the Google Cloud
Run platform for deployment, as well as Firebase for organizing data storage and user management. The
proposed solution provides scalability, efficient use of resources and the ability to centrally update machine
learning models. The results obtained can be used to create intelligent monitoring systems in the agricultural
sector and other industries that require real-time object recognition.
Keywords: cloud computing, mobile application, computer vision, machine learning, pest
identification, YOLO, distributed architecture, microservices

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