OBSERVATIONAL RESEARCH BASED ON SOCIAL MESSAGING DATA TOWARDS QUALITY CLASSIFICATION IN LANDSCAPE PLANTS

This study is based on the analysis of 2,077 messages shared within the first four months of 2025 in a large WhatsApp group comprising landscape plant producers and marketers across Türkiye. The participants include stakeholders from various regions, including growers, contractors, and sellers. The aim of this study is to observe how producers present plant quality in digital communication platforms and to extract a data structure that can inform the QPlantScore quality classification system. Plant names, size, pot volume, and quality-related expressions in the messages were coded through content analysis. Repetitive listings and regional information were also examined for representational distribution. The most frequently mentioned species included Leylandii, Cypress, Laurel, and Thuja. Quality descriptors such as "dense form", "first-class", and "premium" appeared frequently, indicating a lack of standard terminology. The findings highlight the need for a standardized classification system and demonstrate the potential of social messaging data for market-oriented horticultural research.