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Data-driven insights are essential for modern agriculture. This researchpaper introduces a machine learning framework designed to improve how weeducate and reach out to people in the field of horticulture. The frameworkrelies on data from the Horticulture Online Help Desk (HOHD), which is like abig collection of questions from people who love gardening and are part of theExtension Master Gardener Program (EMGP). This framework has two main parts.First, it uses special computer programs (machine learning models) to sortquestions into categories. This helps us quickly send each question to theright expert, so we can answer it faster. Second, it looks at when questionsare asked and uses that information to guess how many questions we might get inthe future and what they will be about. This helps us plan on topics that willbe really important. It's like knowing what questions will be popular in thecoming months. We also take into account where the questions come from bylooking at the Zip Code. This helps us make research that fits the challengesfaced by gardeners in different places. In this paper, we demonstrate thepotential of machine learning techniques to predict trends in horticulture byanalyzing textual queries from homeowners. We show that NLP, classification,and time series analysis can be used to identify patterns in homeowners'queries and predict future trends in horticulture. Our results suggest thatmachine learning could be used to predict trends in other agricultural sectorsas well. If large-scale agriculture industries curate and maintain a comparablerepository of textual data, the potential for trend prediction and strategicagricultural planning could be revolutionized. This convergence of technologyand agriculture offers a promising pathway for the future of sustainablefarming and data-informed agricultural practices

From Text to Trends: A Unique Garden Analytics Perspective on the Future of Modern Agriculture