Design of a GenAI UX layer with small language models for edge computing in smart agriculture

Abstract

This study proposes a framework for designing a GenAI-UX layer in edge computing systems, integrating user experience (UX) principles and small language models (SLM) to optimize human–machine interaction in agricultural environments. Applied to indoor coriander cultivation, the enhanced UX design reduced the harvest cycle from 45 to 32 days by optimizing irrigation and environmental conditions through a voice-controlled interface with audio generation. The implementation of a structured UX approach enabled more efficient interaction with the system, facilitating data-driven decision-making processed at the edge. The study highlights the importance of UX design guidelines aligned with system requirements to ensure accessibility, intuitiveness, and efficiency in embedded environments. The results demonstrate that using SLM in edge systems improves personalization and response speed, enhancing agricultural productivity. This work underscores the need for a reference framework for UX design in embedded systems, ensuring effective interactions and a greater impact on the efficiency of controlled crop environments.