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

翻译：数据驱动的洞察对于现代农业至关重要。本研究提出一个机器学习框架，旨在改善园艺领域的教育与推广方式。该框架依赖于园艺在线帮助台（HOHD）的数据，该平台汇集了来自扩展园丁大师计划（EMGP）中园艺爱好者的海量问题。该框架包含两个主要部分：首先，利用专门的计算机程序（机器学习模型）对问题进行自动分类，从而快速将每个问题分配给相应的专家，提升响应速度；其次，通过分析问题的提出时间，预测未来问题的数量及主题分布，帮助制定重点研究方向。这相当于提前预知未来数月可能流行的热门问题。此外，我们结合邮政编码对问题来源进行地理定位，使研究能够针对不同地区园艺从业者的实际挑战。本文展示了机器学习技术如何通过分析家庭园丁的文本查询来预测园艺趋势。研究表明，自然语言处理（NLP）、分类算法和时间序列分析可用于识别家庭园丁查询模式，并预测园艺领域的未来趋势。我们的结果还表明，机器学习方法可推广至其他农业领域的趋势预测。若大规模农业产业能够建立并维护类似的文本数据存储库，趋势预测与战略性农业规划的能力将迎来革命性突破。这种技术与农业的融合，为可持续农业与数据驱动型农业实践的未来提供了充满希望的路径。