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Smart Agriculture ›› 2022, Vol. 4 ›› Issue (2): 174-182.doi: 10.12133/j.smartag.SA202203013

• 智能管理与控制 • 上一篇    下一篇

基于深度学习的多种农产品供需预测模型

庄家煜1,2(), 许世卫1,2(), 李杨3, 熊露1,2, 刘克宝3, 钟志平1,2   

  1. 1.中国农业科学院农业信息研究所,北京 100081
    2.农业农村部农业信息服务技术重点实验室,北京 100081
    3.黑龙江省农业科学院农业遥感与信息研究所,黑龙江 哈尔滨 150086
  • 收稿日期:2022-03-23 出版日期:2022-06-30
  • 基金资助:
    中国农业科学院创新工程项目(CAAS-ASTIP-2016-AII);中国农业科学院人才项目(JBYW-AII-2022-04);国家自然科学基金项目(21974012)
  • 作者简介:庄家煜(1982-),男,博士,副研究员,研究方向为农业信息分析。E-mail:zhuangjiayu@caas.cn
  • 通信作者: 许世卫(1962-),男,博士,研究员,研究方向为农业信息分析。E-mail:xushiwei@caas.cn

Supply and Demand Forecasting Model of Multi-Agricultural Products Based on Deep Learning

ZHUANG Jiayu1,2(), XU Shiwei1,2(), LI Yang3, XIONG Lu1,2, LIU Kebao3, ZHONG Zhiping1,2   

  1. 1.Agricultural Information Institute of CAAS, Beijing 100081, China
    2.Key Laboratory of Agricultural Information Service Technology of MOA, Beijing 100081, China
    3.Institute of Agricultural Remote Sensing and Information of HAAS, Harbin 150086, China
  • Received:2022-03-23 Online:2022-06-30
  • Supported by:
    Chinese Academy of Agricultural Sciences Innovation Project(CAAS-ASTIP-2016-AII); Talent Program of Chinese Academy of Agricultural Sciences (JBYW-AII-2022-04);National Natural Science Foundation of China(21974012)

摘要:

为进一步提高农产品供需过程模拟与估算精度,本研究以自1980年以来国家级和省级的大量农业数据作为样本,充分考虑农产品品种、时间、收入、经济发展等因素影响,构建基于深度学习长短时记忆神经网络(Long Short-Term Memory Neural Network,LSTM)的多种农产品供需预测模型。模型在充分考虑机理性约束条件的前提下,利用深度学习算法在非线性模型分析预测中的优势,对稻谷、小麦、玉米、大豆、猪肉、禽肉、牛肉、羊肉、水产品等9种主要农产品供需进行分析预测。将基于本模型的2019—2021年产量预测结果与国家统计局公布数据进行对比验证,三年平均预测准确率96.98%,表明本研究构建的预测模型能够高效地反映隐性指标变化对预测结果的影响。该模型可以通过及时地监测农业运行数据,为多区域、跨期的农业展望工作提供智能化技术支持。

关键词: 深度学习, 供需预测模型, 长短时记忆神经网络, 循环神经网络, 农产品产量, 农业展望

Abstract:

To further improve the simulation and estimation accuracy of the supply and demand process of agricultural products, a large number of agricultural data at the national and provincial levels since 1980 were used as the basic research sample, including production, planted area, food consumption, industrial consumption, feed consumption, seed consumption, import, export, price, GDP, population, urban population, rural population, weather and so on, by fully considering the impact factors of agricultural products such as varieties, time, income and economic development, a multi-agricultural products supply and demand forecasting model based on long short-term memory neural network (LSTM) was constructed in this study. The general thought of supply and demand forecasting model is packaging deep neural network training model as an I/O-opening modular model, reserving control interface for input of outside data, and realizing the indicators forecasting of supply and demand and matrixing of balance sheet. The input of model included forecasting balance sheet data of agricultural products, annual price data, general economic data, and international currency data since 2000. The output of model was balance sheet data of next decade since forecasting time. Under the premise of fully considering the mechanical constraints, the model used the advantages of deep learning algorithms in nonlinear model analysis and prediction to analyze and predict supply and demand of 9 main types of agricultural products, including rice, wheat, corn, soybean, pork, poultry, beef, mutton, and aquatic products. The production forecast results of 2019-2021 based on this model were compared and verified with the data published by the National Bureau of Statistics, and the mean absolute percentage error was 3.02%, which meant the average forecast accuracy rate of 2019-2021 was 96.98%. The average forecast accuracy rate was 96.10% in 2019, 98.26% in 2020, and 96.58% in 2021, which shows that with the increase of sample size, the prediction effect of intelligent learning model would gradually get better. The forecasting results indicate that the multi-agricultural supply and demand prediction model based on LSTM constructed in this study can effectively reflect the impact of changes in hidden indicators on the prediction results, avoiding the uncontrollable error introduced by manual experience intervention. The model can provide data production and technical support such as market warning, policy evaluation, resource management and public opinion analysis for agricultural production and management and macroeconomic regulation, and can provide intelligent technical support for multi-regional and inter-temporal agricultural outlook work by monitoring agricultural operation data in a timely manner.

Key words: deep learning, supply and demand forecasting model, LSTM, RNN, agricultural production, agricultural outlook

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