Intelligent Decision Method of Multi-Agricultural Commodity Model Based on Machine Learning

文献类型： 外文期刊

第一作者： Zhuang, Jiayu

作者： Zhuang, Jiayu;Xu, Shiwei;Li, Ganqiong;Zhong, Zhiping

作者机构：

关键词： Agricultural commodity model; machine learning; long short-term memory neural network

期刊名称：INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE （ 影响因子：1.261； 五年影响因子：1.319 ）

ISSN： 0218-0014

年卷期： 2022 年 36 卷 08 期

页码：

收录情况： SCI

摘要： China Agricultural Outlook publishes the outlook report on major agricultural commodities and the estimates of the supply-demand balance sheet of agricultural commodities in the next decade, so as to release agricultural information and guide the development of modern agriculture. A large amount of data has been accumulated in the work associated with agriculture outlook in recent years. This paper will optimize the agriculture commodity model through deep learning and create an analysis tool based on long short-term memory (LSTM) deep learning by comprehensively considering the key factors of supply and demand of agricultural commodities including the output, consumption and price and combining the impact of complex natural, social and economic factors. In this way, the close relevance of different varieties and multi-variable strong coupling of the analysis and prediction model of major agricultural commodities can be solved. The "random sampling" and "stress on causality" of traditional complex agriculture analysis models are replaced by the "whole data" and "emphasis on relevance over causality". The intelligent decision method of agricultural commodity model based on deep learning proposed in this study can effectively improve the analysis efficiency and accuracy of multi-variety coupling model of agricultural commodities (at least by 15%), and enhance the intelligence of the supply-demand analysis and prediction, especially with the accumulation of future data, the prediction accuracy will continue to improve. Machine learning has been regarded as an effective method to provide forecast and early-warning of future agricultural development in a timely manner based on real-time monitoring of agricultural data.

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