Towards Crowd-Based Requirements Engineering for Digital Farming (CrowdRE4DF)

TL;DR

This paper explores a crowd-based approach to requirements engineering in digital farming, introducing a novel application that captures farmers' feedback in situ using speech-to-text and machine learning, with promising preliminary results.

Contribution

It presents the CrowdRE4DF framework and the Farmers' Voice application, enabling in-situ collection of farmers' requirements through speech analysis in noisy farm environments.

Findings

Farmers accepted the technology well

Accurate speech transcription achieved in noisy settings

ML analysis provided useful insights from feedback

Abstract

The farming domain has seen a tremendous shift towards digital solutions. However, capturing farmers' requirements regarding Digital Farming (DF) technology remains a difficult task due to domain-specific challenges. Farmers form a diverse and international crowd of practitioners who use a common pool of agricultural products and services, which means we can consider the possibility of applying Crowd-based Requirements Engineering (CrowdRE) for DF: CrowdRE4DF. We found that online user feedback in this domain is limited, necessitating a way of capturing user feedback from farmers in situ. Our solution, the Farmers' Voice application, uses speech-to-text, Machine Learning (ML), and Web 2.0 technology. A preliminary evaluation with five farmers showed good technology acceptance, and accurate transcription and ML analysis even in noisy farm settings. Our findings help to drive the development of DF technology through in-situ requirements elicitation.