  Data-driven insights are essential for modern agriculture. This research
paper introduces a machine learning framework designed to improve how we
educate and reach out to people in the field of horticulture. The framework
relies on data from the Horticulture Online Help Desk (HOHD), which is like a
big collection of questions from people who love gardening and are part of the
Extension Master Gardener Program (EMGP). This framework has two main parts.
First, it uses special computer programs (machine learning models) to sort
questions into categories. This helps us quickly send each question to the
right expert, so we can answer it faster. Second, it looks at when questions
are asked and uses that information to guess how many questions we might get in
the future and what they will be about. This helps us plan on topics that will
be really important. It's like knowing what questions will be popular in the
coming months. We also take into account where the questions come from by
looking at the Zip Code. This helps us make research that fits the challenges
faced by gardeners in different places. In this paper, we demonstrate the
potential of machine learning techniques to predict trends in horticulture by
analyzing 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 that
machine learning could be used to predict trends in other agricultural sectors
as well. If large-scale agriculture industries curate and maintain a comparable
repository of textual data, the potential for trend prediction and strategic
agricultural planning could be revolutionized. This convergence of technology
and agriculture offers a promising pathway for the future of sustainable
farming and data-informed agricultural practices
