相关实验视频

深度学习启用桃价格预测和实时系统部署在印度的多个市场供应链

F A Shaheen¹, Aqib Gul², Nazir Ganai³

¹Institute of Business and Policy Research, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, J&K, 190025, India. fashaheen@yahoo.com.

Scientific reports

|December 10, 2025

概括

深度学习模型,特别是长期短期记忆 (LSTM) 和变压器,在日常桃价格预测方面显著优于传统方法. 这些先进的AI模型实现了超过92%的准确性,为印度的农业市场情报提供了一个可扩展的框架.

科学领域:

农业经济学农业经济学
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:

对桃等高价值易腐商品的准确价格预测对于供应链效率,生产者收入稳定性和知情决策至关重要.
现有的统计和机器学习模型往往难以捕捉农业商品价格固有的复杂,非线性动态.

研究的目的:

调查深度学习 (DL) 架构的实时每日桃价格预测的有效性.
将DL模型的性能与传统的统计和机器学习 (ML) 方法进行比较.
开发一个实用的,人工智能驱动的咨询工具,用于农业市场情报.

主要方法:

利用了来自印度五个批发市场 (2012-2024) 的每日桃价格数据.
评估了六种预测模型:季节性自回归集成移动平均 (SARIMA),先知,随机森林 (RF),极端梯度增强 (XGBoost),长短期记忆 (LSTM) 和变压器.
在2025年桃季节实施了表现最佳的LSTM模型,作为一个实时的,基于Web的预测系统.

主要成果:

深度学习模型 (LSTM,变压器) 与统计和基于树的方法相比,在捕捉非线性价格波动方面表现出更好的能力.
在现实应用中,LSTM模型实现了超过92%的准确性,其错误率很低 (MAE 5-8,RMSE 8-12),主要市场的sMAPE低于5-10%.
迪博德-马里亚诺 (DM) 测试证实了深度学习方法的统计优势,而不是基线模型.

结论:

深度学习模型为实时农业价格预测提供了强大而准确的解决方案,超越了传统方法.
开发的AI框架提供了一个可扩展和可操作的方法,用于将高级分析集成到印度的农业市场情报系统中.
这项研究为在农业部门利用人工智能创造先例,以加强决策支持和市场稳定.

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