TPTC:主题问题"趋势集群用于智能农业洞察力提取和预测农民的信息需求需求
Samarth Godara1, Shbana Begam2, Ram Swaroop Bana3
1ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India.
Scientific reports
|November 25, 2024
概括
这项研究使用人工智能分析了印度2860万个农民帮助电话电话,确定了农业的关键挑战. 这些发现有助于政策制定者解决农民的需求,并改善全国的农业支持系统.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 随着全球粮食需求的增加,需要解决持续存在的全国性农业相关问题.
- 政策制定者缺乏有关农民面临的全国性挑战的全面数据.
- 以数据为导向的方法对于理解和解决农业问题至关重要.
研究的目的:
- 开发基于人工智能的管道,用于分析印度农民援助需求.
- 为决策者提供可操作的见解,了解农民面临的挑战.
- 预测未来的农民调查趋势.
主要方法:
- 利用印度Kisan呼叫中心的2860万个呼叫日志记录.
- 开发了一个AI管道来提取"主题智能问题的趋势集群" (TPTC).
- 实施和评估7个统计预测模型,包括TBATP1,用于预测通话量.
主要成果:
- 确定了关键趋势和农民报告的问题集群.
- TBATP1预测模型表现出卓越的准确性与低误差率 (RMSE: 0.034,MAE: 0.107).
- 为政府和私营部门的决策产生了可操作的见解.
结论:
- 对农民帮助电话数据的AI分析为农业挑战提供了有价值的见解.
- 预测模型可以预测农民调查量,帮助资源分配.
- TPTC框架支持基于证据的农业发展政策制定.
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